The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Open accessAbstract
Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases.
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In the citing paper
“By definition, FL systems avoid sharing healthcare data among participating institutions.”
From The future of digital health with federated learning (2020) · cites this paper as [63]
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In the citing paper
“It is therefore possible to reconstruct parts of the training data from the algorithm weights themselves on a decentralized node [52][53][54] .”
From Secure, privacy-preserving and federated machine learning in medical imaging (2020) · cites this paper as [54]
In the citing paper
“Attackers can recover data from gradients uploaded by customers [4].”
From Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2021) · cites this paper as [4]
In the citing paper
“But their experiments confirmed that GAN-based approaches might not work well when the selected privacy parameter was smaller than 10 , which is the upper bound of privacy parameter in our experiment, and we can achieve the accuracy of 97% .”
From Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2021) · cites this paper as [4]
In the citing paper
“There are studies discussing potential risks of information leakage in FL [4,66] in which attackers may infer customers' private data from gradients.”
From Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2021) · cites this paper as [4]
In the citing paper
“Hitaj et al. [4] demonstrated that a curious server could obtain the confidential information using the generative adversarial network if gradients were protected by a large privacy budget in the collaborative learning.”
From Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2021) · cites this paper as [4]
In the citing paper
“A combination of both previously cited defenses [60] identifies negatively impacting updates from clients.”
From Vulnerabilities in federated learning (2021) · cites this paper as [60]
In the citing paper
“The reason why gradients can cause privacy leakage is that the gradients are derived from the participants' private training data, and a learning model can be considered as a representation of the high-level statistics of the dataset it was trained on [75].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [75]
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Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
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
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911
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Research works printed but not linked: 2
- Florian Tramèr Fan Zhang Ari Juels Michael K Reiter and Thomas Ristenpart 2016. Stealing Machine Learning Models via Prediction APIs USENIX Security.
- Weilin Xu, Yanjun Qi, and David Evans 2016. Automatically evading classifiers. In NDSS'16.
Cited by
Sorted by citations
Items 201–300 of 1,002
PFLM: Privacy-preserving federated learning with membership proof
Jiang, C., Xu, C., & Zhang, Y. (2021) · Information Sciences · doi:10.1016/j.ins.2021.05.077 · Cited by 86
AI on the edge: a comprehensive review
Su, W., Li, L., Liu, F., He, M., & Liang, X. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10141-4 · Cited by 90
Privacy-Preserving and Byzantine-Robust Federated Learning
Dong, C., Weng, J., Li, M., Liu, J.-N., Liu, Z., Cheng, Y., & Yu, S. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3264697 · Cited by 88
FedPacket: A Federated Learning Approach to Mobile Packet Classification
Bakopoulou, E., Tillman, B., & Markopoulou, A. P. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3058627 · Cited by 89
Privacy-Preserving Auto-Driving: A GAN-Based Approach to Protect Vehicular Camera Data
Xiong, Z., Li, W., Han, Q., & Cai, Z. (2019) · Proceedings · doi:10.1109/icdm.2019.00077 · Cited by 86
NoPeek: Information leakage reduction to share activations in distributed deep learning
Vepakomma, P., Singh, A., Gupta, O., & Raskar, R. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00134 · Cited by 83
Membership Privacy for Machine Learning Models Through Knowledge Transfer
Shejwalkar, V., & Houmansadr, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i11.17150 · Cited by 90
Petersen, E., Potdevin, Y., Mohammadi, E., Zidowitz, S., Breyer, S., Nowotka, D., Henn, S., Pechmann, L., Leucker, M., Rostalski, P., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3178382 · Cited by 92
A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things
Rodríguez, E., Otero, B., & Canal, R. (2023) · Sensors · doi:10.3390/s23031252 · Cited by 82
Gradient Leakage Attack Resilient Deep Learning
Wei, W., & Liu, L. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3139777 · Cited by 87
Poisoning-Assisted Property Inference Attack Against Federated Learning
Wang, Z., Huang, Y., Song, M., Wu, L., Xue, F., & Ren, K. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3196646 · Cited by 86
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy
Zhang, Y., Zeng, D., Luo, J., Xu, Z., & King, I. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587681 · Cited by 89
Zhang, Q., Wang, C., Wu, H., Xin, C., & Phuong, T. V. X. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/547 · Cited by 81
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Khokhar, F. A., Shah, J. H., Khan, M. A., Sharif, M. I., Tariq, U., & Kadry, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107818 · Cited by 82
Tran, H.-Y., Hu, J., Yin, X., & Pota, H. R. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3267892 · Cited by 81
Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey
Rao, B., Zhang, J., Wu, D., Zhu, C., Sun, X., & Chen, B. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3363670 · Cited by 89
Differential privacy in deep learning: A literature survey
Pan, K., Ong, Y.-S., Gong, M., Li, H., Qin, A. K., & Gao, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127663 · Cited by 89
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Yan, X., Cui, B., Xu, Y., Shi, P., & Wang, Z. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2940583 · Cited by 79
Privacy-enhanced multi-party deep learning
Gong, M., Feng, J., & Xie, Y. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.10.001 · Cited by 81
Scalable Privacy-Preserving Distributed Learning
Froelicher, D., Troncoso-Pastoriza, J. R., Pyrgelis, A., Sav, S., Sousa, J. S., Bossuat, J.-P., & Hubaux, J. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2021-0030 · Cited by 82
The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype
Abdulkareem, M., & Petersen, S. E. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.652669 · Cited by 83
Federated Learning with Privacy-preserving and Model IP-right-protection
Yang, Q., Huang, A., Fan, L., Chan, C. S., Lim, J. H., Ng, K. W., Ong, D. S., & Li, B. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1343-2 · Cited by 88
Privacy preservation in Artificial Intelligence and Extended Reality (AI-XR) metaverses: A survey
Alkaeed, M., Qayyum, A., & Qadir, J. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.103989 · Cited by 86
Attack-Resistant Federated Learning with Residual-based ReweightingPreprint
Fu, S., Xie, C., Li, B., & Chen, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11464 · Cited by 91
Spectral sensitivity near exceptional points as a resource for hardware encryption
Yang, M., Zhu, L., Zhong, Q., El‐Ganainy, R., & Chen, P. (2023) · Nature Communications · doi:10.1038/s41467-023-36508-x · Cited by 77
Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models
Pahune, S. A., & Akhtar, Z. (2025) · Information · doi:10.3390/info16020087 · Cited by 80
Cloud-based federated learning implementation across medical centers
Rajendran, S., Obeid, J. S., Binol, H., D`Agostino, R., Foley, K., Zhang, W., Austin, P., Brakefield, J., Gurcan, M. N., & Topaloglu, U. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00060 · Cited by 76
Realizing private and practical pharmacological collaboration
Hie, B., Cho, H., & Berger, B. (2018) · Science · doi:10.1126/science.aat4807 · Cited by 76
Projected federated averaging with heterogeneous differential privacy
Liu, J., Lou, J., Xiong, L., Liu, J., & Meng, X. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3503585.3503592 · Cited by 81
A Simple and Efficient Federated Recommender System
Jalalirad, A., Scavuzzo, M., Capota, C., & Sprague, M. (2019) · IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) · doi:10.1145/3365109.3368788 · Cited by 75
A Privacy-Preserving Federated Learning System for Android Malware Detection Based on Edge Computing
Hsu, R., Wang, Y., Fan, C., Sun, B., Ban, T., Takahashi, T., Wu, T.-W., & Kao, S.-W. (2020) · Asia Joint Conference on Information Security (AsiaJCIS) · doi:10.1109/asiajcis50894.2020.00031 · Cited by 77
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Li, Q., Wen, Z., & He, B. (2019) · arXiv (Cornell University) · Cited by 75
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Jegorova, M., Kaul, C., Mayor, C., O'Neil, A. Q., Weir, A., Murray-Smith, R., & Tsaftaris, S. A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3229593 · Cited by 77
Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback
Minto, L., Haller, M., Livshits, B., & Haddadi, H. (2021) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3460231.3474262 · Cited by 78
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Yang, Z., Shao, B., Xuan, B., Chang, E., & Zhang, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.03915 · Cited by 93
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Zhao, Y., Chen, J., Zhang, J., Wu, D., Teng, J., & Yu, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-38991-8_39 · Cited by 75
Fairness, integrity, and privacy in a scalable blockchain-based federated learning system
Rückel, T., Sedlmeir, J., & Hofmann, P. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108621 · Cited by 80
Sun, Y., Ochiai, H., & Esaki, H. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3133819 · Cited by 77
Shen, M., Wang, H., Zhang, B., Zhu, L., Xu, K., Li, Q. L., & Du, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3028110 · Cited by 73
FedLab: A Flexible Federated Learning FrameworkPreprint
Zeng, D., Liang, S., Hu, X., Wang, H., & Xu, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.11621 · Cited by 78
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Nair, A. K., Raj, E. D., & Sahoo, J. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103723 · Cited by 70
Secure Aggregation is Insecure: Category Inference Attack on Federated Learning
Gao, J., Hou, B., Guo, X., Liu, Z., Zhang, Y., Chen, K., & Li, J. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3128679 · Cited by 71
Multi-Graph Fusion Networks for Urban Region Embedding
Wu, S., Xu, Y. L., Fan, X., Pan, S., Zhu, S., Zheng, C., Cheng, M., & Wang, C. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/321 · Cited by 86
Tramèr, F., Shokri, R., Joaquin, A. S., Le, H., Jagielski, M., Hong, S., & Carlini, N. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560554 · Cited by 79
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and ProspectivePreprint
Jin, Y., Wei, X., Liu, Y., & Yang, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11545 · Cited by 83
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Hu, J., Wang, Z., Yong-sheng, S., Lin, B., Sun, P., Pang, X., Liu, J., & Ren, K. (2023) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2023.3317870 · Cited by 71
Blika, A., Palmos, S., Doukas, G. S., Lamprou, V., Pelekis, S., Kontoulis, M., Ntanos, C., & Askounis, D. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3449563 · Cited by 82
Triastcyn, A., & Faltings, B. V. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2993966 · Cited by 71
Partially Encrypted Multi-Party Computation for Federated Learning
Sotthiwat, E., Zhen, L., Li, Z., & Zhang, C. (2021) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid51090.2021.00101 · Cited by 69
Distributed additive encryption and quantization for privacy preserving federated deep learning
Zhu, H., Wang, R., Jin, Y., Liang, K., & Ning, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.062 · Cited by 71
Pan, Q., Wu, J., Bashir, A. K., Li, J., Yang, W., & Al‐Otaibi, Y. D. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3105492 · Cited by 68
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Xu, G., Zhou, Z., Dong, J., Zhang, L., & Song, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3298196 · Cited by 68
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Chen, Q., Xiang, C., Xue, M., Li, B., Borisov, N., Kaarfar, D., & Zhu, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.02274 · Cited by 84
Safeguarding cross-silo federated learning with local differential privacy
Wang, C., Wu, X., Liu, G., Deng, T., Peng, K., & Wan, S. (2021) · Digital Communications and Networks · doi:10.1016/j.dcan.2021.11.006 · Cited by 67
Federated Learning in Dentistry: Chances and Challenges
Rischke, R., Schneider, L., Müller, K. R., Samek, W., Schwendicke, F., & Krois, J. (2022) · Journal of Dental Research · doi:10.1177/00220345221108953 · Cited by 72
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Zhang, D., Chen, X., Wang, D., & Shi, J. (2018) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc.2018.00104 · Cited by 68
Anomaly detection and defense techniques in federated learning: a comprehensive review
Zhang, C., Yang, S., Mao, L., & Ning, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10796-1 · Cited by 73
A survey of machine learning-based solutions to protect privacy in the Internet of Things
Amiri-Zarandi, M., Dara, R. A., & Fraser, E. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101921 · Cited by 69
Toward Secure and Privacy-Preserving Distributed Deep Learning in Fog-Cloud Computing
Li, Y., Li, H., Xu, G., Xiang, T., Huang, X., & Lu, R. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3012480 · Cited by 66
Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing
Li, Y., Li, H., Xu, G., Xiang, T., & Lu, R. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3150806 · Cited by 68
No Free Lunch Theorem for Security and Utility in Federated Learning
Zhang, X., Gu, H., Fan, L., Chen, K., & Yang, Q. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3563219 · Cited by 74
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Binhammad, M. H. Y., Alqaydi, S., Othman, A., & Abuljadayel, L. (2024) · Journal of Information Security · doi:10.4236/jis.2024.152015 · Cited by 65
Generative AI model privacy: a survey
Liu, Y., Huang, J., Li, Y., Wang, D., & Xiao, B. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11024-6 · Cited by 74
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning
Usynin, D., Ziller, A., Makowski, M. R., Braren, R., Rueckert, D., Glocker, B., Kaissis, G. A., & Passerat‐Palmbach, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00390-3 · Cited by 68
Privacy-Enhanced and Verification-Traceable Aggregation for Federated Learning
Ren, Y., Li, Y., Feng, G., & Zhang, X. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3194930 · Cited by 68
A Detailed Survey on Federated Learning Attacks and Defenses
Sikandar, H. S., Waheed, H., Tahir, S., Malik, S. U. R., & Rafique, W. (2023) · Electronics · doi:10.3390/electronics12020260 · Cited by 64
Zhou, J., Chen, S., Wu, Y., Li, H., Zhang, B., Zhou, L., Hu, Y., Xiang, Z., Li, Z., Chen, N., et al. (2024) · Science Advances · doi:10.1126/sciadv.adh8601 · Cited by 63
Zhao, J., Bagchi, S., Avestimehr, S., Chan, K., Chaterji, S., Dimitriadis, D., Li, J., Li, N., Nourian, A., & Roth, H. (2025) · ACM Computing Surveys · doi:10.1145/3724113 · Cited by 63
A Critical Overview of Privacy in Machine Learning
Cristofaro, E. D. (2021) · IEEE Security & Privacy · doi:10.1109/msec.2021.3076443 · Cited by 63
Federated Learning Attacks and Defenses: A Survey
Chen, Y., Gui, Y., Lin, H., Gan, W., & Wu, Y. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020431 · Cited by 69
Hou, D., Zhang, J., Man, K. L., Ma, J., & Peng, Z. (2021) · Information Communication Technologies Conference (ICTC) · doi:10.1109/ictc51749.2021.9441499 · Cited by 62
Privacy-Preserving Incentive Mechanism Design for Federated Cloud-Edge Learning
Liu, T., Di, B., An, P., & Song, L. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3100096 · Cited by 62
Efficient, Private and Robust Federated Learning
Hao, M., Li, H., Xu, G., Chen, H., & Zhang, T. (2021) · Annual Computer Security Applications Conference · doi:10.1145/3485832.3488014 · Cited by 70
A Survey on Cyber Situation-awareness Systems: Framework, Techniques, and Insights
Alavizadeh, H., Jang‐Jaccard, J., Enoch, S. Y., Al-Sahaf, H., Welch, I. S., Camtepe, S. A., & Kim, D. S. (2022) · ACM Computing Surveys · doi:10.1145/3530809 · Cited by 68
Poongodi, M., Walia, R., Iwendi, C., Ahanger, T. A., Suganthi, S. T., Kamruzzaman, M. M., Bourouis, S., Alhakami, W., & Hamdi, M. (2022) · Expert Systems · doi:10.1111/exsy.13072 · Cited by 63
RetractedPrivacy-Preserving and Verifiable Federated Learning Framework for Edge Computing
Zhou, H., Yang, G., Huang, Y., Dai, H., & Xiang, Y. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3227435 · Cited by 63
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Zhang, C., Yu, S., Tian, Z., & Yu, J. J. Q. (2023) · ACM Computing Surveys · doi:10.1145/3615336 · Cited by 69
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies
Enthoven, D., & Al-Ars, Z. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_8 · Cited by 72
Resource Usage and Performance Trade-offs for Machine Learning Models in Smart Environments
Preuveneers, D., Tsingenopoulos, I., & Joosen, W. (2020) · Sensors · doi:10.3390/s20041176 · Cited by 60
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Yang, X. S., Feng, Y., Fang, W., Shao, J., Tang, X., Xia, S., & Lu, R. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512233 · Cited by 60
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Gu, Y., Bai, Y., & Xu, S. (2022) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2022.103201 · Cited by 61
Balch, J. A., Ruppert, M. M., Loftus, T. J., Guan, Z., Ren, Y., Upchurch, G. R., Ozrazgat‐Baslanti, T., Rashidi, P., & Bihorac, A. (2023) · JMIR Medical Informatics · doi:10.2196/48297 · Cited by 62
Differential Privacy in Deep Learning: An Overview
Ha, T., Dang, T. K., Dang, T. T., Truong, T. A., & Nguyen, M. T. (2019) · International Conference on Advanced Computing and Applications (ACOMP) · doi:10.1109/acomp.2019.00022 · Cited by 67
RDP-GAN: A Rényi-Differential Privacy Based Generative Adversarial Network
Ma, C., Li, J., Ding, M., Liu, B., Wei, K., Weng, J., & Poor, H. V. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3233580 · Cited by 70
COFEL: Communication-Efficient and Optimized Federated Learning with Local Differential Privacy
Lian, Z., Wang, W., & Su, C. (2021) · ICC 2021 - IEEE International Conference on Communications · doi:10.1109/icc42927.2021.9500632 · Cited by 58
Enhancing Privacy via Hierarchical Federated Learning
Wainakh, A., Guinea, A. S., Grube, T., & Mühlhäuser, M. (2020) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw51379.2020.00053 · Cited by 60
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Metadata
- Authors
- Hitaj, B., Ateniese, G., & Perez-Cruz, F.
- Affiliations
- Stevens Institute of Technology (US)
- Stevens Institute of Technology, Hoboken, NJ, USA
- Venue
- Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
- Publisher
- ACM
- Published
- 2017-10-30
- Pages
- 603-618
- ISSN
- 1543-7221 (ISSN-L)
- OpenAlex
- W2591882872
- Semantic Scholar
- 44a97f4eaaefaf5338f8aed2913d5debb2459f7e
- Cited by
- 1,377
- References
- 16
- Citations by year
- 2026: 80 · 2025: 192 · 2024: 214 · 2023: 229 · 2022: 176 · 2021: 204 · 2020: 147 · 2019: 93 · 2018: 39 · 2017: 3
- FWCI
- 22.62
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- ACM SIGSAC Conference on Computer and Communications Security (CCS) (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- generative adversarial networks, deep learning, end-to-end learning, representation learning, classifier training, structured data
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