When Machine Learning Meets Privacy in 6G: A Survey
Abstract
The rapid-developing Artificial Intelligence (AI) technology, fast-growing network traffic, and emerging intelligent applications (e.g., autonomous driving, virtual reality, etc.) urgently require a new, faster, more reliable and flexible network form. At this time, researchers in both industry and academia have turned their attention to the sixth generation (6G) communication networks. In the 6G vision, various intelligent application scenarios that utilize Machine Learning (ML) technology (the most important branch of AI) will bring rich heterogeneous connections, as well as massive information storage and operations. When ML meets 6G, new opportunities will emerge along with numerous privacy challenges. On one hand, a secure ML structure, or the correct application of ML, can protect privacy in 6G. On the other hand, ML may be attacked or abused, resulting in privacy violation. It is worth noting that the alliance between 6G and ML may also be a double-edged sword in many cases, rather than absolutely infringe or protect privacy. Therefore, based on lots of existing meaningful works, this paper aims to provide a comprehensive survey of ML and privacy in 6G, with a view to further promoting the development of 6G and privacy protection technologies.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Multidevice federated learning systems are vulnerable to membership inference attacks when the adversary is a participant.
“There are certain shortcomings of federated learning such as multidevice systems being vulnerable to membership inference attacks when the adversary is a participant [128].”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [128]
Cites
Bitcoin: A Peer-to-Peer Electronic Cash SystemPreprint
Nakamoto, S. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3440802 · Cited by 14,600 · see citing works
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381 · see citing works
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143 · see citing works
Fully homomorphic encryption using ideal lattices
Gentry, C. (2009) · Proceedings of the forty-first annual ACM symposium on Theory of computing · doi:10.1145/1536414.1536440 · Cited by 6,806 · see citing works
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578 · see citing works
Dwork, C. (2006) · Lecture notes in computer science · doi:10.1007/11787006_1 · Cited by 5,945 · see citing works
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems
Saad, W., Bennis, M., & Chen, M. (2020) · IEEE Network · doi:10.1109/mnet.001.1900287 · Cited by 4,985 · see citing works
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754 · see citing works
Warren, S. D., & Brandeis, L. D. (1890) · University of Washington Press eBooks · doi:10.1515/9780295803661-014 · Cited by 4,092 · see citing works
<i>L</i> -diversity: Privacy beyond <i>k</i> -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617 · see citing works
Practical Black-Box Attacks against Machine Learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2017) · Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security · doi:10.1145/3052973.3053009 · Cited by 3,495 · see citing works
(2015) · Cited by 3,471 · see citing works
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451 · see citing works
The EU General Data Protection Regulation (GDPR)
Voigt, P., & von dem Bussche, A. (2017) · doi:10.1007/978-3-319-57959-7 · Cited by 3,001 · see citing works
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · see citing works
6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies
Zhang, Z., Xiao, Y., Ma, Z., Xiao, M., Ding, Z., Lei, X., Karagiannidis, G. K., & Fan, P. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921208 · Cited by 2,575 · see citing works
L-diversity: privacy beyond k-anonymity
Machanavajjhala, A., Gehrke, J. E., Kifer, D., & Venkitasubramaniam, M. (2006) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2006.1 · Cited by 2,397 · see citing works
Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., & Bengio, S. (2016) · arXiv · doi:10.48550/arxiv.1611.01236 · Cited by 2,325 · see citing works
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232 · see citing works
The Roadmap to 6G: AI Empowered Wireless Networks
Letaief, K. B., Chen, W., Shi, Y., Zhang, J., & Zhang, Y.-J. A. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1900271 · Cited by 2,207 · see citing works
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Akhtar, N., & Mian, A. (2018) · IEEE Access · doi:10.1109/access.2018.2807385 · Cited by 2,021 · see citing works
(1978) · Cited by 1,875 · see citing works
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,847 · see citing works
A survey of techniques for internet traffic classification using machine learning
Nguyen, T. T. T., & Armitage, G. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080406 · Cited by 1,664 · see citing works
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566 · see citing works
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
Wang, Y., Chen, Q., Hong, T., & Kang, C. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2818167 · Cited by 1,384 · see citing works
Poisoning Attacks against Support Vector Machines
Biggio, B., Nelson, B., & Laskov, P. (2012) · arXiv · doi:10.48550/arxiv.1206.6389 · Cited by 1,331 · see citing works
Hide and seek: an introduction to steganography
Provos, N., & Honeyman, P. (2003) · IEEE Security & Privacy · doi:10.1109/msecp.2003.1203220 · Cited by 1,295 · see citing works
(2016) · International Conference on Machine Learning · Cited by 1,270 · see citing works
BadNets: Evaluating Backdooring Attacks on Deep Neural Networks
Gu, T., Liu, K., Dolan-Gavitt, B., & Garg, S. (2019) · IEEE Access · doi:10.1109/access.2019.2909068 · Cited by 1,238 · see citing works
Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends
Wang, S., Ouyang, L., Yuan, Y., Ni, X., Han, X., & Wang, F. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2019.2895123 · Cited by 1,194 · see citing works
HiDDeN: Hiding Data With Deep Networks
Zhu, J., Kaplan, R., Johnson, J., & Fei-Fei, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_40 · Cited by 1,186 · see citing works
6G Wireless Communications: Vision and Potential Techniques
Yang, P., Xiao, Y., Xiao, M., & Li, S. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800418 · Cited by 1,153 · see citing works
An overview of multi-task learning
Zhang, Y., & Yang, Q. (2017) · National Science Review · doi:10.1093/nsr/nwx105 · Cited by 1,120 · see citing works
Dalvi, N. N., Domingos, P. M., Mausam, Sanghai, S. K., & Verma, D. (2004) · Knowledge Discovery and Data Mining · doi:10.1145/1014052.1014066 · Cited by 1,085 · see citing works
A Review on the Use of Blockchain for the Internet of Things
Fernandez-Carames, T. M., & Fraga-Lamas, P. (2018) · IEEE Access · doi:10.1109/access.2018.2842685 · Cited by 1,078 · see citing works
Deep Reinforcement Learning framework for Autonomous Driving
Sallab, A. E., Abdou, M., Perot, E., & Yogamani, S. (2017) · Electronic Imaging · doi:10.2352/issn.2470-1173.2017.19.avm-023 · Cited by 1,045 · see citing works
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., & Chen, M. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800286 · Cited by 1,030 · cited 1× · see citing works
“Wang et al. propose using federated learning to enable intelligence in mobile edge computing [175].”
BackgroundFederated Learning over Wireless Networks: Optimization Model Design and Analysis
Tran, N. H., Bao, W., Zomaya, A. Y., Nguyen, M. N. H., & Hong, C. (2019) · IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737464 · Cited by 943 · see citing works
Blockchained On-Device Federated Learning
Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2019.2921755 · Cited by 887 · see citing works
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018) · 2018 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2018.00057 · Cited by 861 · see citing works
The logic theory machine-A complex information processing system
Newell, A., & Simon, H. (1956) · IEEE Transactions on Information Theory · doi:10.1109/tit.1956.1056797 · Cited by 846 · see citing works
Stealing Machine Learning Models via Prediction APIs
Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., & Ristenpart, T. (2016) · arXiv · doi:10.48550/arxiv.1609.02943 · Cited by 796 · see citing works
Blockchain for the Internet of Things: A systematic literature review
Conoscenti, M., Vetro, A., & De Martin, J. C. (2016) · 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2016.7945805 · Cited by 769 · see citing works
Strinati, E. C., Barbarossa, S., González, J. L., Kténas, D., Cassiau, N., Maret, L., & Dehos, C. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921162 · Cited by 749 · see citing works
6G Vision and Requirements: Is There Any Need for Beyond 5G?
David, K., & Berndt, H. (2018) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2018.2848498 · Cited by 729 · see citing works
Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection
Thys, S., Ranst, W. V., & Goedemé, T. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00012 · Cited by 713 · see citing works
Deep Learning for Encrypted Traffic Classification: An Overview
Rezaei, S., & Liu, X. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800819 · Cited by 670 · see citing works
Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.
Fredrikson, M., Lantz, E., Jha, S., Lin, S. M., Page, D., & Ristenpart, T. (2014) · PubMed · Cited by 658 · see citing works
Computing arbitrary functions of encrypted data
Gentry, C. (2010) · Communications of the ACM · doi:10.1145/1666420.1666444 · Cited by 650 · see citing works
Issues and future directions in traffic classification
Dainotti, A., Pescape, A., & Claffy, K. (2012) · IEEE Network · doi:10.1109/mnet.2012.6135854 · Cited by 630 · see citing works
Significant Permission Identification for Machine-Learning-Based Android Malware Detection
Li, J., Sun, L., Yan, Q., Li, Z., Srisa‐an, W., & Ye, H. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2789219 · Cited by 608 · see citing works
Networking and Communications in Autonomous Driving: A Survey
Wang, J., Liu, J., & Kato, N. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2888904 · Cited by 535 · see citing works
Self-citationVerykios, V. S., Elmagarmid, A. K., Bertino, E., Saygın, Y., & Dasseni, E. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.1269668 · Cited by 519 · see citing works
Certified Defenses for Data Poisoning Attacks
Steinhardt, J., Koh, P. W., & Liang, P. (2017) · arXiv · doi:10.48550/arxiv.1706.03691 · Cited by 479 · see citing works
Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing
Li, L., Ota, K., & Dong, M. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2842821 · Cited by 466 · see citing works
Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach
Wang, J., Zhao, L., Liu, J., & Kato, N. (2019) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2019.2902661 · Cited by 450 · see citing works
Self-citationPrivacy-Preserving Machine Learning: Threats and Solutions
Al-Rubaie, M., & Chang, J. M. (2019) · IEEE Security & Privacy · doi:10.1109/msec.2018.2888775 · Cited by 444 · see citing works
Robust Physical-World Attacks on Deep Learning ModelsPreprint
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., & Song, D. X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.08945 · Cited by 443 · see citing works
Shen, M., Tang, X., Zhu, L., Du, X., & Guizani, M. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2901840 · Cited by 443 · see citing works
Robust Smartphone App Identification via Encrypted Network Traffic Analysis
Taylor, V. F., Spolaor, R., Conti, M., & Martinovic, I. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2737970 · Cited by 439 · cited 1× · see citing works
“build a system which can feasibly identify smartphone Apps by fingerprinting their network traffic [149].”
BackgroundSoK: Towards the Science of security and privacy in machine learning
Papernot, N., McDaniel, P. D., Sinha, A., & Wellman, M. P. (2018) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 427 · see citing works
Meidan, Y., Bohadana, M., Shabtai, A., Guarnizo, J., Ochoa, M., Tippenhauer, N. O., & Elovici, Y. (2017) · ACM Symposium on Applied Computing · doi:10.1145/3019612.3019878 · Cited by 424 · see citing works
ML Confidential: Machine Learning on Encrypted Data
Graepel, T., Lauter, K., & Naehrig, M. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-37682-5_1 · Cited by 423 · see citing works
A Survey on Internet Traffic Identification
Callado, A. C., Kamienski, C., Szabó, G., Gero, B. P., Kelner, J., Fernandes, S., & Sadok, D. (2009) · IEEE Communications Surveys and Tutorials · doi:10.1109/surv.2009.090304 · Cited by 421 · see citing works
Automatic Steganographic Distortion Learning Using a Generative Adversarial Network
Tang, W., Tan, S., Li, B., & Huang, J. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2745572 · Cited by 421 · see citing works
6G Technologies: Key Drivers, Core Requirements, System Architectures, and Enabling Technologies
Zong, B., Fan, C., Wang, X., Duan, X., Wang, B., & Wang, J. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921398 · Cited by 419 · see citing works
Self-citationSecurity Evaluation of Pattern Classifiers under Attack
Biggio, B., Fumera, G., & Roli, F. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.57 · Cited by 412 · see citing works
Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence
Kato, N., Fadlullah, Z. M., Tang, F., Mao, B., Tani, S., Okamura, A., & Liu, J. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1800365 · Cited by 407 · see citing works
Self-citationDemystifying Membership Inference Attacks in Machine Learning as a Service
Truex, S., Liu, L., Gürsoy, M. E., Yu, L., & Wei, W. (2019) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2897554 · Cited by 389 · see citing works
6G Visions: Mobile ultra-broadband, super internet-of-things, and artificial intelligence
Zhang, L., Liang, Y.-C., & Niyato, D. (2019) · China Communications · doi:10.23919/jcc.2019.08.001 · Cited by 354 · see citing works
A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks
Hu, D., Wang, L., Jiang, W., Zheng, S., & Li, B. (2018) · IEEE Access · doi:10.1109/access.2018.2852771 · Cited by 342 · see citing works
Dwork, C. (2011) · Springer eBooks · doi:10.1007/978-1-4419-5906-5_752 · Cited by 341 · see citing works
Differentially Private Generative Adversarial NetworkPreprint
Xie, L., Lin, K., Wang, S., Wang, F., & Zhou, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06739 · Cited by 322 · see citing works
Support Vector Machines Under Adversarial Label Noise
Biggio, B., Nelson, B. A., & Laskov, P. (2011) · UNICA IRIS Institutional Research Information System (University of Cagliari) · Cited by 319 · see citing works
CNN-Based Adversarial Embedding for Image Steganography
Tang, W., Li, B., Tan, S., Barni, M., & Huang, J. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2891237 · Cited by 316 · see citing works
An Ensemble Random Forest Algorithm for Insurance Big Data Analysis
Lin, W., Wu, Z., Lin, L., Wen, A., & Li, J. (2017) · IEEE Access · doi:10.1109/access.2017.2738069 · Cited by 311 · see citing works
Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics
Zhang, K., Zhu, Y., Leng, S., He, Y., Maharjan, S., & Zhang, Y. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2903191 · Cited by 309 · see citing works
Mobile-Edge Computation Offloading for Ultradense IoT Networks
Guo, H., Liu, J., Zhang, J., Sun, W. Y., & Kato, N. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2838584 · Cited by 308 · see citing works
Self-citationThe reusable holdout: Preserving validity in adaptive data analysis
Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O., & Roth, A. (2015) · Science · doi:10.1126/science.aaa9375 · Cited by 307 · see citing works
Offline/realtime traffic classification using semi-supervised learning
Erman, J., Mahanti, A., Arlitt, M., Cohen, I., & Williamson, C. (2007) · Performance Evaluation · doi:10.1016/j.peva.2007.06.014 · Cited by 287 · see citing works
Ferrara, M., Franco, A., & Maltoni, D. (2014) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2014.6996240 · Cited by 282 · see citing works
In-Vehicle Network Attacks and Countermeasures: Challenges and Future Directions
Liu, J., Zhang, S., Sun, W., & Shi, Y. (2017) · IEEE Network · doi:10.1109/mnet.2017.1600257 · Cited by 281 · see citing works
Self-citationEdge Intelligence and Blockchain Empowered 5G Beyond for the Industrial Internet of Things
Zhang, K., Zhu, Y., Maharjan, S., & Zhang, Y. (2019) · IEEE Network · doi:10.1109/mnet.001.1800526 · Cited by 276 · see citing works
Analyzing Android Encrypted Network Traffic to Identify User Actions
Conti, M., Mancini, L., Spolaor, R., & Verde, N. V. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2478741 · Cited by 275 · see citing works
Blockchain Access Privacy: Challenges and Directions
Henry, R., Herzberg, A., & Kate, A. (2018) · IEEE Security & Privacy · doi:10.1109/msp.2018.3111245 · Cited by 273 · see citing works
Steganographic generative adversarial networks
Volkhonskiy, D., Nazarov, I., & Burnaev, E. V. (2020) · International Conference on Machine Vision · doi:10.1117/12.2559429 · Cited by 266 · see citing works
Learning and Recognizing the Places We Go
Hightower, J. R., Consolvo, S., LaMarca, A., Smith, I., & Hughes, J. (2005) · Lecture notes in computer science · doi:10.1007/11551201_10 · Cited by 261 · see citing works
Machine Learning Based Trust Computational Model for IoT Services
Jayasinghe, U., Lee, G. M., Um, T.-W., & Shi, Q. (2019) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2018.2839623 · Cited by 256 · see citing works
SSGAN: Secure Steganography Based on Generative Adversarial Networks
Shi, H., Dong, J., Wang, W., Qian, Y., & Zhang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77380-3_51 · Cited by 253 · see citing works
Deep Learning Based Inference of Private Information Using Embedded Sensors in Smart Devices
Liang, Y., Cai, Z., Yu, J., Han, Q., & Li, Y. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700349 · Cited by 245 · see citing works
Good Word Attacks on Statistical Spam Filters.
Lowd, D., & Meek, C. (2005) · Cited by 240 · see citing works
Generating Steganographic Images via Adversarial TrainingPreprint
Hayes, J., & Danezis, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00371 · Cited by 235 · see citing works
Finn, R. L., Wright, D., & Friedewald, M. (2013) · European Data Protection: Coming of Age · doi:10.1007/978-94-007-5170-5_1 · Cited by 226 · see citing works
Squeezing Deep Learning into Mobile and Embedded Devices
Lane, N. D., Bhattacharya, S., Mathur, A., Georgiev, P., Forlivesi, C., & Kawsar, F. (2017) · IEEE Pervasive Computing · doi:10.1109/mprv.2017.2940968 · Cited by 224 · see citing works
When Machine Learning Meets Blockchain: A Decentralized, Privacy-preserving and Secure Design
Chen, X., Ji, J., Luo, C., Liao, W., & Li, P. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622598 · Cited by 224 · see citing works
Towards the Science of Security and Privacy in Machine LearningPreprint
Papernot, N., McDaniel, P. D., Sinha, A., & Wellman, M. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.03814 · Cited by 217 · see citing works
Ma, Z., Ge, H., Liu, Y., Zhao, M., & Ma, J. (2019) · IEEE Access · doi:10.1109/access.2019.2896003 · Cited by 215 · see citing works
Sarwate, A. D., & Chaudhuri, K. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2259911 · Cited by 211 · see citing works
A general framework for adversarial examples with objectives
Sharif, M., Bhagavatula, S., Bauer, L., & Reiter, M. K. (2019) · ACM Transactions on Privacy and Security · doi:10.1145/3317611 · Cited by 200 · see citing works
Dynamic Differential Privacy for ADMM-Based Distributed Classification Learning
Zhang, T., & Zhu, Q. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2607691 · Cited by 198 · see citing works
Privacy-Preserving ECG Classification With Branching Programs and Neural Networks
Barni, M., Failla, P., Lazzeretti, R., Sadeghi, A., & Schneider, T. H. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2108650 · Cited by 195 · see citing works
Security Testbed for Internet-of-Things Devices
Siboni, S., Sachidananda, V., Meidan, Y., Bohadana, M., Mathov, Y., Bhairav, S., Shabtai, A., & Elovici, Y. (2018) · IEEE Transactions on Reliability · doi:10.1109/tr.2018.2864536 · Cited by 191 · see citing works
Extracting places from traces of locations
Kang, J. H., Welbourne, W., Stewart, B., & Borriello, G. (2005) · ACM SIGMOBILE Mobile Computing and Communications Review · doi:10.1145/1094549.1094558 · Cited by 186 · see citing works
Big Data Privacy Preserving in Multi-Access Edge Computing for Heterogeneous Internet of Things
Du, M., Wang, K., Chen, Y., Wang, X., & Sun, Y. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1701148 · Cited by 178 · see citing works
Self-citationOn Attacking Statistical Spam Filters.
Wittel, G. L., & Wu, S. F. (2004) · Cited by 172 · see citing works
Distributed Privacy-Preserving Collaborative Intrusion Detection Systems for VANETs
Zhang, T., & Zhu, Q. (2018) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2018.2801622 · Cited by 168 · see citing works
Human Activity Recognition Using Federated Learning
Sozinov, K., Vlassov, V., & Girdzijauskas, S. (2018) · 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) · doi:10.1109/bdcloud.2018.00164 · Cited by 167 · see citing works
AI-Enabled Future Wireless Networks: Challenges, Opportunities, and Open Issues
Elsayed, M., & Erol-Kantarci, M. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2919236 · Cited by 157 · see citing works
Biometric authentication: system security and user privacy
Jain, A. K., & Nandakumar, K. (2012) · Computer · doi:10.1109/mc.2012.364 · Cited by 157 · see citing works
Rahulamathavan, Y., Phan, R. C. ‐., Veluru, S., Cumanan, K., & Rajarajan, M. (2014) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2013.51 · Cited by 153 · see citing works
Blockchain for Managing Heterogeneous Internet of Things: A Perspective Architecture
Tseng, L., Wong, L., Otoum, S., Aloqaily, M., & Ben‐Othman, J. (2020) · IEEE Network · doi:10.1109/mnet.001.1900103 · Cited by 150 · see citing works
EPIC: A Differential Privacy Framework to Defend Smart Homes Against Internet Traffic Analysis
Liu, J., Zhang, C., & Fang, Y. M. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2799820 · Cited by 141 · see citing works
Self-citationAutomobile Driver Fingerprinting: A New Machine Learning Based Authentication Scheme
Xun, Y., Liu, J., Kato, N., Fang, Y., & Zhang, Y. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2946626 · Cited by 141 · see citing works
Self-citationDAPASA: Detecting Android Piggybacked Apps Through Sensitive Subgraph Analysis
Fan, M. Y., Liu, J., Wang, J., Li, H., Tian, Z., & Liu, T. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2687880 · Cited by 140 · see citing works
Self-citationPoisoning behavioral malware clustering
Biggio, B., Rieck, K., Ariu, D., Wressnegger, C., Corona, I., Giacinto, G., & Roli, F. (2014) · doi:10.1145/2666652.2666666 · Cited by 139 · see citing works
Cost-friendly Differential Privacy for Smart Meters: Exploiting the Dual Roles of the Noise
Zhang, Z., Qin, Z., Zhu, L., Weng, J., & Ren, K. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2585963 · Cited by 139 · see citing works
Oblivious Neural Network Computing via Homomorphic Encryption
Orlandi, C., Piva, A., & Barni, M. (2007) · EURASIP Journal on Information Security · doi:10.1155/2007/37343 · Cited by 138 · see citing works
Wang, G., Shi, Z., Nixon, M., & Han, S. (2019) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain.2019.00030 · Cited by 137 · see citing works
Deep Learning for Mobile Multimedia: A Survey
Ota, K., Dao, M. S., Mezaris, V., & Natale, F. G. B. D. (2017) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3092831 · Cited by 135 · see citing works
Moustafa, N., Choo, K. R., Radwan, I., & Camtepe, S. A. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2890808 · Cited by 130 · see citing works
Non-Intrusive Load Monitoring by Novel Neuro-Fuzzy Classification Considering Uncertainties
Lin, Y., & Tsai, M. (2014) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2014.2314738 · Cited by 130 · see citing works
Granell, R., Axon, C. J., & Wallom, D. C. H. (2014) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2014.2377213 · Cited by 126 · see citing works
Detection of Face Morphing Attacks by Deep Learning
Seibold, C., Samek, W., Hilsmann, A., & Eisert, P. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-64185-0_9 · Cited by 123 · see citing works
Localization and Navigation in Autonomous Driving: Threats and Countermeasures
Luo, Q., Cao, Y., Liu, J., & Benslimane, A. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800533 · Cited by 120 · see citing works
Self-citationTwo-Stream Federated Learning: Reduce the Communication Costs
Yao, X., Huang, C., & Sun, L. (2018) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip.2018.8698609 · Cited by 118 · see citing works
Security Evaluation of Support Vector Machines in Adversarial Environments
Biggio, B., Corona, I., Nelson, B. A., Rubinstein, B. I. P., Maiorca, D., Fumera, G., Giacinto, G., & Roli, F. (2014) · Springer eBooks · doi:10.1007/978-3-319-02300-7_4 · Cited by 116 · see citing works
Wang, W., Gao, Z., Zhao, M., Li, Y., Liu, J., & Zhang, X. (2018) · IEEE Access · doi:10.1109/access.2018.2835654 · Cited by 116 · see citing works
Self-citationAdversarial Examples Against Deep Neural Network based Steganalysis
Zhang, Y., Zhang, W., Chen, K., Liu, J., Liu, Y., & Yu, N. (2018) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3206004.3206012 · Cited by 114 · see citing works
Self-citationEfficient Privacy-Preserving k-Nearest Neighbor Search
Qi, Y., & Atallah, M. J. (2008) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2008.79 · Cited by 109 · see citing works
Lip Reading-Based User Authentication Through Acoustic Sensing on Smartphones
Lü, L., Yu, J., Chen, Y. J., Liu, H., Zhu, Y., Kong, L., & Li, M. (2019) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2019.2891733 · Cited by 109 · see citing works
Energy Disaggregation via Learning Powerlets and Sparse Coding
Elhamifar, E., & Sastry, S. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9249 · Cited by 108 · see citing works
When Smart Wearables Meet Intelligent Vehicles: Challenges and Future Directions
Sun, W. Y., Liu, J., & Zhang, H. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1600423 · Cited by 107 · see citing works
Self-citationIs data clustering in adversarial settings secure?
Biggio, B., Pillai, I., Bulò, S. R., Ariu, D., Pelillo, M., & Roli, F. (2013) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2517312.2517321 · Cited by 105 · see citing works
I Know What You Saw Last Minute—Encrypted HTTP Adaptive Video Streaming Title Classification
Dubin, R., Dvir, A. Z., Pele, O., & Hadar, O. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2730819 · Cited by 99 · see citing works
The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis
Conti, M., Li, Q.-Q., Maragno, A., & Spolaor, R. (2017) · IEEE Communications Surveys and Tutorials · doi:10.1109/comst.2018.2843533 · Cited by 97 · see citing works
Multi-Gait Recognition Based on Attribute Discovery
Chen, X., Weng, J., Lu, W., & Xu, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2726061 · Cited by 86 · see citing works
FD-GAN: Face De-Morphing Generative Adversarial Network for Restoring Accomplice’s Facial Image
Peng, F., Zhang, L., & Long, M. (2019) · IEEE Access · doi:10.1109/access.2019.2920713 · Cited by 85 · see citing works
Security Analysis of Online Centroid Anomaly DetectionPreprint
Kloft, M., & Laskov, P. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1003.0078 · Cited by 83 · see citing works
My Privacy My Decision: Control of Photo Sharing on Online Social Networks
Xu, K., Guo, Y., Guo, L., Fang, Y. M., & Li, X. (2015) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2015.2443795 · Cited by 83 · see citing works
Secure Phrase Search for Intelligent Processing of Encrypted Data in Cloud-Based IoT
Shen, M., Ma, B., Zhu, L., Du, X., & Xu, K. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2871607 · Cited by 83 · see citing works
TSP Security in Intelligent and Connected Vehicles: Challenges and Solutions
Li, Y., Luo, Q., Liu, J., Guo, H., & Kato, N. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1800289 · Cited by 83 · see citing works
Self-citationExploiting machine learning techniques for location recognition and prediction with smartphone logs
Cho, S. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.079 · Cited by 80 · see citing works
Gunasinghe, H., & Bertino, E. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2777787 · Cited by 78 · see citing works
Converging Blockchain and Machine Learning for Healthcare
Vyas, S., Gupta, M., & Yadav, R. K. (2019) · doi:10.1109/aicai.2019.8701230 · Cited by 77 · see citing works
Poisoning attacks to compromise face templates
Biggio, B., Didaci, L., Fumera, G., & Roli, F. (2013) · International Conference on Biometrics · doi:10.1109/icb.2013.6613006 · Cited by 76 · see citing works
Learning Human Activities through Wi-Fi Channel State Information with Multiple Access Points
Li, H., Ota, K., Dong, M., & Guo, M. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700083 · Cited by 73 · see citing works
Securing Deep Learning Based Edge Finger Vein Biometrics With Binary Decision Diagram
Yang, W., Wang, S., Hu, J., Zheng, G., Yang, J., & Valli, C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2900665 · Cited by 73 · see citing works
Spatial Image Steganography Based on Generative Adversarial NetworkPreprint
Yang, J., Liu, K., Kang, X., Wong, E. K., & Shi, Y.-Q. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.07939 · Cited by 69 · see citing works
Zhang, H., Zhang, Q., Liu, J., & Guo, H. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2844287 · Cited by 67 · see citing works
Self-citationEnsemble Method for Privacy-Preserving Logistic Regression Based on Homomorphic Encryption
Cheon, J. H., Kim, D., Kim, Y., & Song, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2866697 · Cited by 67 · see citing works
A framework for quantitative security analysis of machine learning
Laskov, P., & Kloft, M. (2009) · doi:10.1145/1654988.1654990 · Cited by 66 · see citing works
Nie, L., Li, Y., & Kong, X. (2018) · IEEE Access · doi:10.1109/access.2018.2854842 · Cited by 66 · see citing works
Deep Reinforcement Learning for Social-Aware Edge Computing and Caching in Urban Informatics
Zhang, K., Cao, J., Liu, H., Maharjan, S., & Zhang, Y. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2953189 · Cited by 66 · see citing works
Attacker Identification and Intrusion Detection for In-Vehicle Networks
Ning, J., Wang, J., Liu, J., & Kato, N. (2019) · IEEE Communications Letters · doi:10.1109/lcomm.2019.2937097 · Cited by 66 · see citing works
Self-citationDENDRAL and Meta-DENDRAL: roots of knowledge systems and expert system applications
Feigenbaum, E. A., & Buchanan, B. G. (1993) · Artificial Intelligence · doi:10.1016/0004-3702(93)90191-d · Cited by 65 · see citing works
Preserving Model Privacy for Machine Learning in Distributed Systems
Jia, Q., Guo, L., Jin, Z., & Fang, Y. M. (2018) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2018.2809624 · Cited by 65 · see citing works
Transforming health policy through machine learning
Ashrafian, H., & Darzi, A. W. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002692 · Cited by 63 · see citing works
Location Privacy in Usage-Based Automotive Insurance: Attacks and Countermeasures
Zhou, L., Du, S., Zhu, H., Chen, C., Ota, K., & Dong, M. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2848227 · Cited by 62 · see citing works
Wireless Telematics Systems in Emerging Intelligent and Connected Vehicles: Threats and Solutions
Luo, Q., & Liu, J. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1700364 · Cited by 60 · see citing works
Self-citationReconstruction Attacks Against Mobile-Based Continuous Authentication Systems in the Cloud
Al-Rubaie, M., & Chang, J. M. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2594132 · Cited by 57 · see citing works
Six Key Enablers for Machine Type Communication in 6GPreprint
Mahmood, N. H., Alves, H., López, O. L. A., Shehab, M. A., Osorio, D. P. M., & Latva‐aho, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.05406 · Cited by 54 · see citing works
EdgeSanitizer: Locally Differentially Private Deep Inference at the Edge for Mobile Data Analytics
Xu, C., Ren, J., She, L., Zhang, Y., Qin, Z., & Ren, K. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2897005 · Cited by 52 · see citing works
Interpret Federated Learning with Shapley ValuesPreprint
Wang, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04519 · Cited by 51 · see citing works
Comparison of Different Methods for Next Location Prediction
Petzold, J., Bagci, F., Trumler, W., & Ungerer, T. (2006) · Lecture notes in computer science · doi:10.1007/11823285_96 · Cited by 51 · see citing works
Big Data Mining of Users’ Energy Consumption Patterns in the Wireless Smart Grid
Zhu, L., Li, M., Zhang, Z., Du, X., & Guizani, M. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1700157 · Cited by 51 · see citing works
Privacy Preserving K-nearest Neighbor Classification
Zhan, J., Chang, L., & Matwin, S. (2005) · Cited by 50 · see citing works
Towards Federated Learning Approach to Determine Data Relevance in Big Data
Doku, R., Rawat, D. B., & Liu, C. (2019) · IEEE International Conference on Information Reuse and Integration for Data Science (IRI) · doi:10.1109/iri.2019.00039 · Cited by 49 · see citing works
Encrypted statistical machine learning: new privacy preserving methodsPreprint
Aslett, L. J. M., Esperança, P. M., & Holmes, C. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.06845 · Cited by 48 · see citing works
AlkylVM: A Virtual Machine for Smart Contract Blockchain Connected Internet of Things
Ellul, J., & Pace, G. J. (2018) · IFIP International Conference on New Technologies, Mobility and Security (NTMS) · doi:10.1109/ntms.2018.8328732 · Cited by 48 · see citing works
Poisoning Complete-Linkage Hierarchical Clustering
Biggio, B., Bulò, S. R., Pillai, I., Mura, M. L., Zemene, E., Pelillo, M., & Roli, F. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44415-3_5 · Cited by 48 · see citing works
Pafko, P., & Mach, J. (2013) · PubMed · Cited by 45 · see citing works
Finkel, J. R., & Manning, C. D. (2010) · Cited by 43 · see citing works
SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol
Jiang, Y., Hamer, J., Wang, C., Jiang, X., Kim, M., Song, Y., Xia, Y., Mohammed, N., Sadat, M. N., & Wang, S. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2833463 · Cited by 43 · see citing works
Huguenin, K., Bilogrevic, I., Machado, J. S., Mihaila, S., Shokri, R., Dacosta, I., & Hubaux, J. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2741958 · Cited by 42 · see citing works
Demographic Information Inference through Meta-Data Analysis of Wi-Fi Traffic
Li, H., Zhu, H., & Ma, D. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2753244 · Cited by 39 · see citing works
Efficient Privacy-Preserving Machine Learning in Hierarchical Distributed System
Jia, Q., Guo, L., Fang, Y. M., & Wang, G. (2018) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2018.2859420 · Cited by 37 · see citing works
Privacy Regulation Aware Process Mapping in Geo-Distributed Cloud Data Centers
Zhou, A. C., Xiao, Y., Gong, Y., He, B., Zhai, J., & Mao, R. (2019) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2019.2896894 · Cited by 37 · see citing works
Privacy-Preserving Distributed Learning via Obfuscated Stochastic Gradients
Gade, S., & Vaidya, N. H. (2018) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2018.8619133 · Cited by 34 · see citing works
Distributed Differentially Private Algorithms for Matrix and Tensor Factorization
Imtiaz, H., & Sarwate, A. D. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2018.2877842 · Cited by 32 · see citing works
Synchronized Detection and Recovery of Steganographic Messages with Adversarial Learning
Shi, H., Zhang, X., Wang, S., Fu, G. Y., & Tang, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22741-8_3 · Cited by 31 · see citing works
Client–Server Multitask Learning From Distributed Datasets
Dinuzzo, F., Pillonetto, G., & Nicolao, G. D. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2095882 · Cited by 30 · see citing works
Zhang, Z., Jiang, X., & Xi, H. (2016) · China Communications · doi:10.1109/cc.2016.7563684 · Cited by 28 · see citing works
Side-Channel Analysis for Intelligent and Connected Vehicle Security: A New Perspective
Xun, Y., Liu, J., & Zhang, Y. (2019) · IEEE Network · doi:10.1109/mnet.001.1900214 · Cited by 25 · see citing works
Self-citationQoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities
Pan, W., & Cheng, G. (2018) · IEEE Access · doi:10.1109/access.2018.2811416 · Cited by 24 · see citing works
A Word-Embedding-Based Steganalysis Method for Linguistic Steganography via Synonym Substitution
Xiang, L., Yu, J., Yang, C., Zeng, D., & Shen, X. (2018) · IEEE Access · doi:10.1109/access.2018.2878273 · Cited by 24 · see citing works
Datoo, A. (2018) · Computer Fraud & Security · doi:10.1016/s1361-3723(18)30088-5 · Cited by 23 · see citing works
What You Submit Is Who You Are: A Multimodal Approach for Deanonymizing Scientific Publications
Payer, M., Huang, L., Gong, N. Z., Borgolte, K., & Frank, M. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2368355 · Cited by 21 · see citing works
Adversarial Spam Detection Using the Randomized Hough Transform-Support Vector Machine
DeBarr, D., Sun, H., & Wechsler, H. (2013) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2013.61 · Cited by 20 · see citing works
On (The Lack Of) Location Privacy in Crowdsourcing Applications
Boukoros, S., Humbert, M., Katzenbeisser, S., & Troncoso, C. (2019) · SERVAL (Université de Lausanne) · doi:10.48550/arxiv.1901.04923 · Cited by 20 · see citing works
PPolyNets: Achieving High Prediction Accuracy and Efficiency With Parametric Polynomial Activations
Wu, W., Liu, J., Wang, H., Tang, F., & Xian, M. (2018) · IEEE Access · doi:10.1109/access.2018.2882407 · Cited by 18 · see citing works
Self-citationLarge Margin Gaussian Mixture Models with Differential Privacy
Pathak, M. A., & Raj, B. (2012) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2012.27 · Cited by 16 · see citing works
Privacy Maintenance Collaborative Data Mining - A Practical Approach
Raju, R. P. K., Komalavalli, R., & Kesavakumar, V. (2009) · doi:10.1109/icetet.2009.184 · Cited by 11 · see citing works
Utsunomiya, Y., Toyoda, K., & Sasase, I. (2016) · Journal of Information Processing · doi:10.2197/ipsjjip.24.109 · Cited by 8 · see citing works
A New Scheme to Privacy-Preserving Collaborative Data Mining
Zhu, J. (2009) · International Conference on Information Assurance and Security (IAS) · doi:10.1109/ias.2009.133 · Cited by 8 · see citing works
HEMIN: A Cryptographic Approach for Private k-NN Classification.
Kumar, P., Singh, M., & Saxena, A. (2008) · DMIN · Cited by 6 · see citing works
Security and privacy in unified communications: Challenges and solutions
Καρόπουλος, Γ., Portokalidis, G., Domingo‐Ferrer, J., Lin, Y., Geneiatakis, D., & Kambourakis, G. (2015) · Computer Communications · doi:10.1016/j.comcom.2015.08.004 · Cited by 6 · see citing works
Weakening the Detecting Capability of CNN-based SteganalysisPreprint
Ma, S., Guan, Q., Zhao, X., & Liu, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10889 · Cited by 5 · see citing works
Communications and Privacy under Surveillance
Lin, Y., Voas, J. M., Pescapè, A., & Müeller, P. R. (2016) · Computer · doi:10.1109/mc.2016.65 · Cited by 4 · see citing works
Redefining Security Criteria for Networking Devices with Case Studies
Lin, Y., Lee, C., & Tsai, H.-C. (2014) · IEEE Security & Privacy · doi:10.1109/msp.2013.76 · Cited by 3 · see citing works
Flagged first, then by citations
2 of the 203 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges
Nguyen, V.-L., Lin, P.-C., Cheng, B.-C., Hwang, R.-H., & Lin, Y.-D. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3108618 · Cited by 515 · see citing works
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges
McEnroe, P., Wang, S., & Liyanage, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176400 · Cited by 482 · see citing works
The Roadmap to 6G Security and Privacy
Porambage, P., Gür, G., Osorio, D. P. M., Liyanage, M., Gurtov, A., & Ylianttila, M. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3078081 · Cited by 422 · see citing works
Security and Privacy on 6G Network Edge: A Survey
Mao, B., Liu, J., Wu, Y., & Kato, N. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3244674 · Cited by 323 · see citing works
Self-citationThe Roadmap of Communication and Networking in 6G for the Metaverse
Tang, F., Chen, X., Zhao, M., & Kato, N. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.019.2100721 · Cited by 289 · see citing works
Self-citationAI and 6G Security: Opportunities and Challenges
Siriwardhana, Y., Porambage, P., Liyanage, M., & Ylianttila, M. (2021) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit51104.2021.9482503 · Cited by 280 · see citing works
Mahboob, S., & Liu, L. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3347145 · Cited by 264 · see citing works
When Federated Learning Meets Privacy-Preserving Computation
Chen, J., Yan, H., Liu, Z., Zhang, M., Xiong, H., & Yu, S. (2024) · ACM Computing Surveys · doi:10.1145/3679013 · Cited by 260 · see citing works
Yu, Y., Tang, J., Huang, J., Zhang, X. Y., So, D. K. C., & Wong, K. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3089476 · Cited by 247 · see citing works
Enabling Intelligent Connectivity: A Survey of Secure ISAC in 6G Networks
Zhu, X., Liu, J., Lu, L., Zhang, T., Qiu, T., Wang, C., & Liu, Y. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3432871 · Cited by 213 · see citing works
Self-citationDigital-Twin-Assisted Task Offloading Based on Edge Collaboration in the Digital Twin Edge Network
Liu, T., Tang, L., Wang, W., Chen, Q., & Zeng, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3086961 · Cited by 211 · see citing works
Energy-Efficient Fog Computing for 6G-Enabled Massive IoT: Recent Trends and Future Opportunities
Malik, U. M., Javed, M. A., Zeadally, S., & Islam, S. U. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3068056 · Cited by 200 · see citing works
A Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications
Zuo, Y., Guo, J., Gao, N., Zhu, Y., Jin, S., & Li, X. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315374 · Cited by 206 · see citing works
Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks
Coronado, E., Behravesh, R., Subramanya, T., Fernández–Fernández, A., Siddiqui, M. S., Costa‐Pérez, X., & Riggio, R. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3212586 · Cited by 186 · see citing works
Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges
Wang, S., Qureshi, M. A., Miralles-Pechuán, L., Huynh-The, T., Gadekallu, T. R., & Liyanage, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3386872 · Cited by 170 · see citing works
Machine and Deep Learning for Resource Allocation in Multi-Access Edge Computing: A Survey
Djigal, H., Xu, J. H., Liu, L., & Zhang, Y. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3199544 · Cited by 173 · see citing works
Quy, V. K., Chehri, A., Quy, N. M., Han, N. D., & Ban, N. T. (2023) · IEEE Access · doi:10.1109/access.2023.3269297 · Cited by 164 · see citing works
Ferrag, M. A., Friha, O., Kantarcı, B., Tihanyi, N., Cordeiro, L. C., Debbah, M. A., Hamouda, D., Al-Hawawreh, M. S., & Choo, K. R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3317242 · Cited by 170 · see citing works
Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152 · see citing works
“On the other hand, the wider adoption of machine learning applications in wireless networks has prompted a multitude of ongoing research and development endeavors in the field of wireless communications.”
BackgroundArtificial Intelligence in 6G Wireless Networks: Opportunities, Applications, and Challenges
Alhammadi, A., Shayea, I. A. M., El‐Saleh, A. A., Azmi, M. H., Ismail, Z. H., Kouhalvandi, L., & Saad, S. A. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/8845070 · Cited by 157 · see citing works
6G Security Challenges and Potential Solutions
Porambage, P., Gür, G., Osorio, D. P. M., Liyanage, M., & Ylianttila, M. (2021) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit51104.2021.9482609 · Cited by 138 · see citing works
Evolution of optical wireless communication for B5G/6G
Wei, Z., Wang, Z., Zhang, J., Li, Q., Zhang, J., & Fu, H. Y. (2022) · Progress in Quantum Electronics · doi:10.1016/j.pquantelec.2022.100398 · Cited by 125 · see citing works
A vision on the artificial intelligence for 6G communication
Ahammed, T. B., Patgiri, R., & Nayak, S. (2022) · ICT Express · doi:10.1016/j.icte.2022.05.005 · Cited by 131 · see citing works
Vehicular intelligence in 6G: Networking, communications, and computing
Guo, H., Zhou, X., Liu, J., & Zhang, Y. (2021) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100399 · Cited by 125 · see citing works
Self-citationXu, S., Liu, J., Cao, Y., Li, J., & Zhang, Y. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3055965 · Cited by 113 · see citing works
Self-citationFrom 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey
Mahdi, M. N., Ahmad, A. R., Qassim, Q. S., Natiq, H., Subhi, M. A., & Mahmoud, M. A. (2021) · Applied Sciences · doi:10.3390/app11178117 · Cited by 110 · see citing works
Blockchain and AI technology convergence: Applications in transportation systems
Singh, P., Elmi, Z., Lau, Y., Borowska-Stefańska, M., Wiśniewski, S., & Dulebenets, M. A. (2022) · Vehicular Communications · doi:10.1016/j.vehcom.2022.100521 · Cited by 108 · see citing works
Toward 6G: An Overview of the Next Generation of Intelligent Network Connectivity
Tera, S. P., Chinthaginjala, R., Pau, G., & Kim, T. H. (2024) · IEEE Access · doi:10.1109/access.2024.3523327 · Cited by 113 · see citing works
6G vision: An ultra-flexible perspective
Arslan, H., Doğan-Tusha, S., & Yazar, A. (2020) · ITU Journal on Future and Evolving Technologies · doi:10.52953/ikvy9186 · Cited by 102 · see citing works
Advanced Deep Learning Models for 6G: Overview, Opportunities, and Challenges
Jiao, L., Shao, Y., Sun, L., Liu, F., Yang, S., Ma, W., Li, L., Liu, X., Hou, B., Zhang, X., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3418900 · Cited by 111 · see citing works
6G: The Intelligent Network of Everything
Pennanen, H., Hänninen, T., Tervo, O., Tölli, A., & Latva‐aho, M. (2024) · IEEE Access · doi:10.1109/access.2024.3521579 · Cited by 117 · see citing works
A Survey on Trust Models in Heterogeneous Networks
Wang, J., Yan, Z., Wang, H., Li, T., & Pedrycz, W. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3192978 · Cited by 98 · see citing works
Self-citationReinforcement Learning-Based Physical Cross-Layer Security and Privacy in 6G
Lu, X., Xiao, L., Li, P., Ji, X., Xu, C., Yu, S., & Zhuang, W. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3224279 · Cited by 105 · see citing works
Fouda, M. M., Fadlullah, Z. M., Ibrahem, M. I., & Kato, N. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3486690 · Cited by 100 · see citing works
Self-citationIntelligent Reflecting Surface Empowered Physical-Layer Security: Signal Cancellation or Jamming?
Xu, S., Liu, J., & Cao, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3079325 · Cited by 96 · see citing works
Self-citationAerial Computing: A New Computing Paradigm, Applications, and Challenges
Pham, Q., Ruby, R., Fang, F., Nguyen, D. C., Yang, Z., Le, M., Ding, Z., & Hwang, W. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3160691 · Cited by 98 · see citing works
Deep Learning-Based Privacy Preservation and Data Analytics for IoT Enabled Healthcare
Bi, H., Liu, J., & Kato, N. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3117285 · Cited by 91 · see citing works
Self-citationAbubakar, A. I., Ahmad, I., Omeke, K. G., Öztürk, M., Öztürk, C., Abdel-Salam, A. M., Mollel, M. S., Abbasi, Q. H., Hussain, S., & Imran, M. A. (2023) · Drones · doi:10.3390/drones7030214 · Cited by 86 · see citing works
Kazmi, S. H. A., Hassan, R. B., Qamar, F., Nisar, K., & Ibrahim, A. A. A. (2023) · Symmetry · doi:10.3390/sym15061147 · Cited by 86 · see citing works
Elaziz, M. A., Al‐qaness, M. A. A., Dahou, A., Alsamhi, S. H., Abualigah, L., Ibrahim, R. A., & Ewees, A. A. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1521 · Cited by 90 · see citing works
Kalla, A., de Alwis, C., Porambage, P., Gür, G., & Liyanage, M. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100404 · Cited by 82 · see citing works
Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges
Tang, Q., Yu, F. R., Xie, R., Boukerche, A., Huang, T., & Liu, Y. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3175453 · Cited by 78 · see citing works
A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures
Wang, X., Zhao, Z., Yi, L., Ning, Z., Guo, L., Yu, F. R., & Guo, S. (2024) · ACM Computing Surveys · doi:10.1145/3703625 · Cited by 77 · see citing works
A survey: Distributed Machine Learning for 5G and beyond
Nassef, O., Sun, W., Purmehdi, H., Tatipamula, M., & Mahmoodi, T. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.108820 · Cited by 76 · see citing works
Secure IoT Healthcare Architecture with Deep Learning‐Based Access Control System
Thilagam, K., Beno, A., Lakshmi, M. V., Wilfred, C. B., George, S. M., Karthikeyan, M., Vijayakumar, P., Ramesh, C., & Karunakaran, P. (2022) · Journal of Nanomaterials · doi:10.1155/2022/2638613 · Cited by 75 · see citing works
Security concerns on machine learning solutions for 6G networks in mmWave beam prediction
Çatak, F. Ö., Kuzlu, M., Çatak, E., Cali, Ü., & Ünal, D. (2022) · Physical Communication · doi:10.1016/j.phycom.2022.101626 · Cited by 65 · see citing works
Alhaj, N. A., Jamlos, M. F., Manap, S. A., Abdelsalam, S., Bakhit, A. A., Mamat, R., Jamlos, M. A., Gismalla, M. S. M., & Hamdan, M. (2023) · IEEE Access · doi:10.1109/access.2023.3345453 · Cited by 61 · see citing works
Virtual Reality Solutions Employing Artificial Intelligence Methods: A Systematic Literature Review
de Oliveira, T. R., Rodrigues, B. B., da Silva, M. M., Spinasse, R. A. N., Ludke, G. G., Gaudio, M. R. S., Gomes, G. I. R., Cotini, L. G., Vargens, D. D. S., Schimidt, M. Q., et al. (2022) · ACM Computing Surveys · doi:10.1145/3565020 · Cited by 73 · see citing works
Zhang, S., Zhu, D., & Liu, Y. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110255 · Cited by 60 · see citing works
Nguyen, H. T., Nguyễn, M. T., Do, H. T., Hua, H. T., & Nguyen, C. V. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/5051328 · Cited by 56 · see citing works
Fair and Energy-Efficient Coverage Optimization for UAV Placement Problem in the Cellular Network
Liu, Y., Huangfu, W., Zhou, H., Zhang, H., Liu, J., & Long, K. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3170615 · Cited by 55 · see citing works
Self-citationFuture 6G Communications Powering Vertical Handover in Non-Terrestrial Networks
Warrier, A., Aljaburi, L., Whitworth, H., Al–Rubaye, S., & Tsourdos, A. (2024) · IEEE Access · doi:10.1109/access.2024.3371906 · Cited by 54 · see citing works
Data Privacy and Security in Autonomous Connected Vehicles in Smart City Environment
Alam, T. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8090095 · Cited by 55 · see citing works
AF Relaying Secrecy Performance Prediction for 6G Mobile Communication Networks in Industry 5.0
Xu, L., Zhou, X., Tao, Y., Yu, X., Yu, M., & Khan, F. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3120511 · Cited by 50 · see citing works
Integration of Sensing, Communication, and Computing for Metaverse: A Survey
Wang, X., Guo, Q., Ning, Z., Guo, L., Wang, G., Gao, X., & Zhang, Y. (2024) · ACM Computing Surveys · doi:10.1145/3659946 · Cited by 50 · see citing works
Majeed, A., Khan, S., & Hwang, S. O. (2022) · IEEE Access · doi:10.1109/access.2022.3175219 · Cited by 49 · see citing works
Handover Management for Drones in Future Mobile Networks—A Survey
Shayea, I. A. M., Dushi, P., Banafaa, M., Rashid, R. A., Ali, S., Sarijari, M. A., Daradkeh, Y. I., & Mohamad, H. (2022) · Sensors · doi:10.3390/s22176424 · Cited by 50 · see citing works
Blockchain-based group authentication scheme for 6G communication network
Soni, M., & Singh, D. K. (2023) · Physical Communication · doi:10.1016/j.phycom.2023.102005 · Cited by 49 · see citing works
Wu, Y., Ma, Y., Dai, H., & Wang, H. H. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107743 · Cited by 47 · see citing works
Oprescu, A. M., Amarante, G. M., García‐Díaz, L., Rey, V. E., Chimenea, Á., Martinez-Martinez, R. L., & Ternero, M. D. C. R. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.011 · Cited by 45 · see citing works
A comprehensive review on deep learning algorithms: Security and privacy issues
Tayyab, M. A., Marjani, M., Jhanjhi, N. Z., Hashem, M., Usmani, R. S. A., & Qamar, F. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103297 · Cited by 47 · see citing works
Jameil, A. K., & Al-Raweshidy, H. S. (2024) · IEEE Access · doi:10.1109/access.2024.3420741 · Cited by 42 · see citing works
A survey on privacy for B5G/6G: New privacy challenges, and research directions
Sandeepa, C., Siniarski, B., Kourtellis, N., Wang, S., & Liyanage, M. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100405 · Cited by 41 · see citing works
Physical Layer Security for 6G: Toward Achieving Intelligent Native Security at Layer-1
Ara, I., & Kelley, B. (2024) · IEEE Access · doi:10.1109/access.2024.3413047 · Cited by 40 · see citing works
Toward Virtualized Optical-Wireless Heterogeneous Networks
Vujičić, Z., Santos, M. C., Méndez, R., Klaiqi, B., Rodrı́guez, J., Gelabert, X., Rahman, M. A., & Gaudino, R. (2024) · IEEE Access · doi:10.1109/access.2024.3417358 · Cited by 40 · see citing works
Optimization Design for Federated Learning in Heterogeneous 6G Networks
Luo, B., Han, P., Sun, P., Ouyang, X., Huang, J., & Ding, N. (2023) · IEEE Network · doi:10.1109/mnet.006.2200437 · Cited by 39 · see citing works
A survey of secure semantic communications
Meng, R., Gao, S., Fan, D., Gao, H., Wang, Y., Xu, X., Wang, B., Lv, S., Zhang, Z., Sun, M., et al. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104181 · Cited by 40 · see citing works
Cybertwin-Driven Resource Provisioning for IoE Applications at 6G-Enabled Edge Networks
Adhikari, M., Ambigavathi, M., Kumar, N., & Srirama, S. N. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3096672 · Cited by 39 · see citing works
6G Cloud-Native System: Vision, Challenges, Architecture Framework and Enabling Technologies
Li, Q., Ding, Z., Tong, X., Wu, G., Stojanovski, S., Luetzenkirchen, T., Kolekar, A., Bangolae, S., & Palat, S. (2022) · IEEE Access · doi:10.1109/access.2022.3205341 · Cited by 37 · see citing works
Exploring science-technology linkages: A deep learning-empowered solution
Chen, X., Ye, P., Huang, L., Wang, C., Cai, Y., Deng, L., & Ren, H. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103255 · Cited by 40 · see citing works
Guo, J., Wang, M., Yin, H., Song, B., Chi, Y., Yu, F. R., & Yuen, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3496491 · Cited by 38 · see citing works
Ogundokun, R. O., Awotunde, J. B., Imoize, A. L., Li, C., Abdulahi, A. T., Adelodun, A. B., Sur, S. N., & Lee, C. (2023) · Sustainability · doi:10.3390/su15097315 · Cited by 36 · see citing works
Ahmad, S. F., Alam, M. M., Rahmat, M. K., Shahid, M. K., Aslam, M., Salim, N. A., & Al‐Abyadh, M. H. A. (2023) · Sustainability · doi:10.3390/su15086540 · Cited by 37 · see citing works
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis
Yang, L., Rajab, M. E., Shami, A. A., & Muhaidat, S. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3376631 · Cited by 37 · see citing works
Pandi, V. S., Albert, A. J., Thapa, K. N. K., & Krishnaprasanna, R. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101669 · Cited by 31 · see citing works
Exploiting Deep Learning for Secure Transmission in an Underlay Cognitive Radio Network
Zhang, M., Cumanan, K., Thiyagalingam, J., Tang, Y., Wang, W., Ding, Z., & Dobre, O. A. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3050104 · Cited by 29 · see citing works
Reviewing wireless broadband technologies in the peak smartphone era: 6G versus Wi-Fi 7 and 8
Oughton, E. J., Geraci, G., Polese, M., Shah, V. K., Bubley, D., & Blue, S. (2024) · Telecommunications Policy · doi:10.1016/j.telpol.2024.102766 · Cited by 27 · see citing works
A Comprehensive Survey of 6G Wireless CommunicationsPreprint
Zhao, Y., Zhai, W., Zhao, J., Zhang, T., Sun, S., Niyato, D. T., & Lam, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03889 · Cited by 44 · see citing works
Toward Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks
Yang, L., Naser, S. A., Shami, A. A., Muhaidat, S., Ong, L. Y., & Debbah, M. A. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3547764 · Cited by 26 · see citing works
Successive Point-of-Interest Recommendation With Personalized Local Differential Privacy
Bao, T., Xu, L., Zhu, L., Wang, L., & Li, T. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3108463 · Cited by 25 · see citing works
Wireless Blockchain Meets 6G: The Future Trustworthy and Ubiquitous Connectivity
Luo, H., Sun, G., Wang, J., Yu, H., Niyato, D. T., Dustdar, S., & Han, Z. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3593918 · Cited by 23 · see citing works
Self-citationEnhanced Interference Management for 6G in-X Subnetworks
Adeogun, R., Berardinelli, G., & Mogensen, P. (2022) · IEEE Access · doi:10.1109/access.2022.3170694 · Cited by 23 · see citing works
6G and AI: The Emergence of Future Forefront Technology
Ahammed, T. B., & Patgiri, R. (2020) · Advanced Communication Technologies and Signal Processing (ACTS) · doi:10.1109/acts49415.2020.9350396 · Cited by 22 · see citing works
A review on machine learning techniques for network security
Nazir, R., Laghari, A. A., Dahri, F. H., Yin, S., Alhakeem, Z. M., Hakim, H., & Mughal, Z. A. (2025) · Journal of Cyber Security Technology · doi:10.1080/23742917.2025.2480730 · Cited by 20 · see citing works
Loss-Privacy Tradeoff in Federated Edge Learning
Liu, T., Di, B., Wang, B., & Song, L. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3161786 · Cited by 20 · see citing works
Multi-Access Edge Offloading Based on Physical Layer Security in C-V2X System
Qin, J., & Liu, J. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3164896 · Cited by 20 · see citing works
Self-citationA brief survey on 6G communications
Rajoria, S., & Mishra, K. (2022) · Wireless Networks · doi:10.1007/s11276-022-03007-8 · Cited by 20 · see citing works
Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators
Perera, D., Wang, Y., Lin, C., Nguyen, H. T., & Chai, R. (2022) · Sensors · doi:10.3390/s22166230 · Cited by 20 · see citing works
Two Stage Beamforming in Massive MIMO: A Combinatorial Multi-Armed Bandit Based Approach
Song, Y., Liu, C., Zhang, W., Liu, Y., Zhou, H., & Shen, X. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3229312 · Cited by 20 · see citing works
Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning
Momynkulov, Z., Dosbayev, Z., Suliman, A., Abduraimova, B., Смайлов, Н., Zhekambayeva, M., & Zhamangarin, D. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.036205 · Cited by 20 · see citing works
Lopez, M. A., Barbosa, G. N. N., & Mattos, D. M. F. (2022) · International Conference on 6G Networking (6GNet) · doi:10.1109/6gnet54646.2022.9830402 · Cited by 19 · see citing works
Designing a 6G Testbed for Location: Use Cases, Challenges, Enablers and Requirements
Khatib, E. J., Alvarez-Merino, C. S., Luo-Chen, H. Q., & Barco, R. (2023) · IEEE Access · doi:10.1109/access.2023.3240775 · Cited by 18 · see citing works
6G-IoT Framework for Sustainable Smart City: Vision and Challenges
Mishra, P., & Singh, G. K. (2023) · Springer eBooks · doi:10.1007/978-3-031-33354-5_5 · Cited by 18 · see citing works
AI empowered 6G technologies and network layers: Recent trends, opportunities, and challenges
Rashid, H. U., & Jeong, S.-H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125985 · Cited by 17 · see citing works
Discussion on a new paradigm of endogenous security towards 6G networks
Ji, X., Wu, J., Jin, L., Huang, K., Chen, Y., Sun, X., You, W., Huo, S., & Yang, J. (2022) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2200060 · Cited by 18 · see citing works
Devi, P., Bharti, M. R., & Gautam, D. (2025) · Vehicular Communications · doi:10.1016/j.vehcom.2025.100891 · Cited by 17 · see citing works
Lightweight privacy-preserving predictive maintenance in 6G enabled IIoT
Li, S., Li, S., & Min, G. (2024) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2023.100548 · Cited by 16 · see citing works
An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks
Hamid, A. K., Al‐Wesabi, F. N., Nemri, N., Zahary, A. T., & Khan, I. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.020309 · Cited by 16 · see citing works
Towards 6G Communications: Architecture, Challenges, and Future Directions
Mitra, P., Bhattacharjee, R., Chatterjee, T., De, S., Karmakar, R., Ghosh, A., & Adhikari, T. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9580084 · Cited by 16 · see citing works
AI and Blockchain Enabled Future Wireless Networks: A Survey And Outlook
Zhou, Z., Onireti, O., Xu, H., Zhang, L., & Imran, M. (2024) · Distributed Ledger Technologies Research and Practice · doi:10.1145/3644369 · Cited by 15 · see citing works
- Authors
- Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N.
- Affiliations
- Xidian University (CN)
- Northwestern Polytechnical University (CN)
- Tohoku University (JP)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2020
- Volume
- 22
- Issue
- 4
- Pages
- 2694-2724
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W3044837284
- Cited by
- 211
- References
- 203
- Citations by year
- 2026: 10 · 2025: 29 · 2024: 50 · 2023: 38 · 2022: 44 · 2021: 33 · 2020: 7
- FWCI
- 11.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Funders
- National Natural Science Foundation of China (61771374, 61771373, 61801360, 61601357)
- Natural Science Basic Research Program of Shaanxi (2020JC-15, 2020JM-109)
- Fundamental Research Funds for the Central Universities (3102019PY005, 31020190QD040, 31020200QD010)
- Special Funds for Central Universities Construction of World-Class Universities (Disciplines) and Special Development Guidance (06390-20GH020114)
- China 111 Project (B16037)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Computer Networks and Communications › Cybersecurity and Information Systems
- Keywords
- 6G, machine learning, privacy preservation, privacy risks, secure machine learning, intelligent networks, privacy violations, artificial intelligence in communication
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review