Local Differential Privacy-Based Federated Learning for Internet of Things
Open accessAbstract
The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users' location information, traffic information, motor vehicle information, environmental information, etc., which raises severe sensitive personal information privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this article, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The proposed Three-Outputs mechanism introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB. Finally, an LDP-FedSGD algorithm is proposed to coordinate the cloud server and vehicles to train the model collaboratively. Extensive experimental results on real-world data sets validate that our proposed algorithms are capable of protecting privacy while guaranteeing utility.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
A real-world healthcare FL experiment in a network of 100 hospitals using clump thickness as physiological attributes shows low resource consumption and good privacy protection.
“A real-world healthcare FL experiment is tested in a network of 100 hospitals with using clump thickness as physiological attributes for building training samples, showing a low resource consumption and good privacy protection.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [185]
The paper describes privacy-protecting federated learning and a mechanism designed to reduce communication cost (p. 2). But its Experiments section (pp. 14–15) describes vehicle, activity-recognition, and census datasets—not a healthcare experiment involving 100 hospitals or clump thickness. The claimed setting and attributes are unsupported. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272 · see citing works
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557 · see citing works
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856 · see citing works
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · cited 1× · see citing works
“Our datasets include the following: (i) the WISDM Human Activity Recognition dataset [38], which is a set of accelerometer data on an Android phone from 35 subjects performing 6 activities, where the domain of the timestamps of the phone’s uptime is removed from the dataset, and the remaining 3…”
MethodFederated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565 · see citing works
Self-citationRAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Erlingsson, Ú., Pihur, V., & Korolova, A. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660348 · Cited by 1,838 · see citing works
Mechanism Design via Differential Privacy
McSherry, F., & Talwar, K. (2007) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2007.66 · Cited by 1,379 · see citing works
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377 · see citing works
Towards Sharp Inapproximability For Any 2-CSP
Austrin, P. (2007) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2007.41 · Cited by 1,293 · see citing works
A Hybrid Approach to Privacy-Preserving Federated Learning
Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., & Zhou, Y. (2019) · Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3338501.3357370 · Cited by 1,029 · see citing works
Learning Differentially Private Recurrent Language Models
McMahan, H. B., Ramage, D., Talwar, K., & Zhang, L. (2017) · arXiv · doi:10.48550/arxiv.1710.06963 · Cited by 849 · see citing works
Verifynet: Secure and verifiable federated learning
Xu, G., Li, H., Liu, S., Yang, K., & Lin, X. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2929409 · Cited by 839 · see citing works
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., & Liu, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3017377 · Cited by 613 · see citing works
Self-citationRevisiting Distributed Synchronous SGDPreprint
Pan, X., Chen, J., Monga, R., Samy Bengio, A., & Józefowicz, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05800 · Cited by 608 · see citing works
Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence
Hao, M., Li, H., Luo, X., Xu, G., Yang, H., & Liu, S. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2945367 · Cited by 569 · see citing works
Vehicle classification in distributed sensor networks
Duarte, M. F., & Hu, Y. H. (2004) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2004.03.020 · Cited by 507 · see citing works
Fair Resource Allocation in Federated Learning
Li, T., Sanjabi, M., Beirami, A., & Smith, V. (2019) · arXiv · doi:10.48550/arxiv.1905.10497 · Cited by 475 · see citing works
Local, Private, Efficient Protocols for Succinct Histograms
Bassily, R., & Smith, A. (2015) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2746539.2746632 · Cited by 425 · see citing works
Collecting and Analyzing Multidimensional Data with Local Differential Privacy
Wang, N., Xiao, X., Yang, Y., Zhao, J., Hui, S. C., Shin, H., Shin, J., & Yu, G. (2019) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2019.00063 · Cited by 408 · see citing works
Self-citationLocally Differentially Private Protocols for Frequency Estimation
Wang, T., Blocki, J., Li, N., & Jha, S. (2017) · USENIX Security Symposium · Cited by 400 · see citing works
Self-citationMinimax Optimal Procedures for Locally Private Estimation
Duchi, J. C., Jordan, M. I., & Wainwright, M. J. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1389735 · Cited by 390 · see citing works
Extremal Mechanisms for Local Differential PrivacyPreprint
Kairouz, P., Oh, S., & Viswanath, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.1338 · Cited by 383 · see citing works
Personalized Federated Learning With Differential Privacy
Hu, R., Guo, Y., Li, H., Pei, Q., & Gong, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991416 · Cited by 350 · see citing works
Local Differential Privacy for Deep Learning
Arachchige, P. C. M., Bertök, P., Khalil, I., Liu, D., Camtepe, S. A., & Atiquzzaman, M. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2952146 · Cited by 300 · see citing works
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks
Saputra, Y. M., Hoang, D. T., Nguyen, D. N., Dutkiewicz, E., Mueck, M. D., & Srikanteswara, S. (2019) · 2019 IEEE Global Communications Conference (GLOBECOM) · doi:10.1109/globecom38437.2019.9013587 · Cited by 277 · see citing works
Towards Fair and Privacy-Preserving Federated Deep Models
Lyu, L., Yu, J., Nandakumar, K., Li, Y., Ma, X., Jin, J., Yu, H., & Ng, K. S. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2996273 · Cited by 250 · cited 1× · see citing works
“Recently, LDP has attracted much attention [13]–[20].”
Self-citationThreats to federated learning A survey
Lyu, L., Yu, H., & Yang, Q. (2020) · arXiv · doi:10.48550/arxiv.2003.02133 · Cited by 221 · see citing works
Self-citationFederated Learning with Bayesian Differential Privacy
Triastcyn, A., & Faltings, B. V. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005465 · Cited by 213 · see citing works
Privacy-Preserving Collaborative Deep Learning With Unreliable Participants
Zhao, L., Wang, Q., Zou, Q., Zhang, Y., & Chen, Y. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2939713 · Cited by 210 · see citing works
Privacy Preserving Data Aggregation Scheme for Mobile Edge Computing Assisted IoT Applications
Li, X., Liu, S., Wu, F., Kumari, S., & Rodrigues, J. J. P. C. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2874473 · Cited by 208 · see citing works
Ruggles, S., Flood, S., Goeken, R., et al. (2020) · Institute for Social Research and Data Innovation · doi:10.18128/d010.v10.0 · Cited by 173 · see citing works
Plausible deniability for privacy-preserving data synthesis
Bindschaedler, V., Shokri, R., & Gunter, C. A. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3055540.3055542 · Cited by 156 · see citing works
Federated Learning in Mobile Edge Networks: A Comprehensive SurveyPreprint
Lim, W. Y. B., Luong, N. C., Hoang, D. T., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11875 · Cited by 138 · see citing works
Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection
Zhao, L., Hu, S., Wang, Q., Jiang, J., Chao, S., Luo, X., & Hu, P. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2020.2986205 · Cited by 127 · see citing works
Secure Data Aggregation of Lightweight E-Healthcare IoT Devices With Fair Incentives
Tang, W., Ren, J., Kun, D., & Zhang, Y. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2923261 · Cited by 123 · see citing works
Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework
Wang, Y., Tong, Y., & Shi, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6096 · Cited by 122 · see citing works
Privacy-Aware Offloading in Mobile-Edge Computing
He, X., Liu, J., Jin, R., & Dai, H. (2017) · Globecom · doi:10.1109/glocom.2017.8253985 · Cited by 112 · see citing works
Preserving Privacy in the Internet of Connected Vehicles
Ghane, S., Jolfaei, A., Kulik, L., Ramamohanarao, K., & Puthal, D. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2964410 · Cited by 105 · see citing works
Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy
Gürsoy, M. E., Tamersoy, A., Truex, S., Wei, W., & Liu, L. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2949041 · Cited by 87 · see citing works
On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks
Sun, M., & Tay, W. P. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2929446 · Cited by 74 · see citing works
A survey of local differential privacy for securing internet of vehicles
Zhao, P., Zhang, G., Wan, S., Liu, G., & Umer, T. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-019-03104-0 · Cited by 66 · see citing works
Fog-Embedded Deep Learning for the Internet of Things
Lyu, L., Bezdek, J. C., He, X., & Jin, J. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2912465 · Cited by 61 · see citing works
Self-citationGuaranteeing Local Differential Privacy on Ultra-Low-Power Systems
Choi, W.-S., Tomei, M., Vicarte, J. R. S., Hanumolu, P. K., & Kumar, R. (2018) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2018.00053 · Cited by 54 · see citing works
Privacy-Preserving Blockchain-Based Federated Learning for IoT DevicesPreprint
Zhao, Y., Zhao, J., Jiang, L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10893 · Cited by 54 · see citing works
Self-citationFederated learning framework for mobile edge computing networks
Fantacci, R., & Picano, B. (2019) · CAAI Transactions on Intelligence Technology · doi:10.1049/trit.2019.0049 · Cited by 53 · 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
Singular Spectrum Analysis for Local Differential Privacy of Classifications in the Smart Grid
Ou, L., Qin, Z., Liao, S., Li, T., & Zhang, D. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2977220 · Cited by 45 · see citing works
Collaborative Learning on the Edges: A Case Study on Connected Vehicles
Lu, S., Yao, Y., & Shi, W. (2019) · Cited by 43 · see citing works
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using SketchesPreprint
Li, T., Liu, Z., Sekar, V., & Smith, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00972 · Cited by 40 · see citing works
Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas
Wang, T., Zhao, J., Yu, H., Liu, J., Yang, X., Ren, X., & Shi, S. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357954 · Cited by 15 · see citing works
Self-citationBiSample: Bidirectional Sampling for Handling Missing Data with Local Differential Privacy
Sun, L., Ye, X., Zhao, J., Lu, C., & Yang, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59410-7_6 · Cited by 13 · see citing works
Self-citationDistributed Clustering in the Anonymized Space with Local Differential PrivacyPreprint
Sun, L., Zhao, J., & Ye, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.11441 · Cited by 10 · see citing works
Self-citationThe Podium Mechanism: Improving on the Laplace and Staircase MechanismsPreprint
Pihur, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.00191 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [6][6] J. Duchi, M. J. Wainwright, and M. I. Jordan, "Local privacy and minimax bounds: Sharp rates for probability estimation," in Advances in Neural Information Processing Systems, 2013, pp. 1529–1537. -
- [25][25] X. He, J. Liu, R. Jin, and H. Dai, "Privacy-aware offloading in mobileedge computing," in GLOBECOM 2017-2017 IEEE Global Communications Conference. IEEE, 2017, pp. 1–6. -
- [38][38] E. Bagdasaryan, O. Poursaeed, and V. Shmatikov, "Differential privacy has disparate impact on model accuracy," in Advances in Neural Information Processing Systems, 2019, pp. 15 453–15 462. -
Cited by
Sorted by citations
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503 · see citing works
“[102] IoT privacy preservation HFL DNN Vehicles Cloud server An FL-differential privacy-based scheme for privacy enhancement in vehicular IoT.”
BackgroundMethodA Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407 · see citing works
Self-citationFederated learning on non-IID data: A survey
Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062 · see citing works
A Survey on Federated Learning for Resource-Constrained IoT Devices
Imteaj, A., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3095077 · Cited by 774 · see citing works
Federated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661 · see citing works
Self-citationYin, X., Zhu, Y., & Hu, J. (2022) · ACM Computing Surveys · doi:10.1145/3460427 · Cited by 638 · see citing works
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548 · see citing works
Self-citationLyu, L., Yu, H., Zhao, J., & Yang, Q. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_1 · Cited by 444 · see citing works
Self-citationBrauneck, A., Schmalhorst, L., Kazemi Majdabadi, M. M., Bakhtiari, M., Völker, U., Baumbach, J., Baumbach, L., & Buchholtz, G. (2023) · Journal of Medical Internet Research · doi:10.2196/41588 · Cited by 130 · see citing works
Kalapaaking, A. P., Khalil, I., & Yi, X. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2023.3268186 · Cited by 122 · see citing works
DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learning
Hossain, M. T., Islam, S., Badsha, S., & Shen, H. (2021) · 2021 17th International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn53354.2021.00038 · Cited by 44 · see citing works
Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach
Li, K., Zheng, J., Yuan, X., Ni, W., Akan, O. B., & Poor, H. V. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3362147 · Cited by 41 · see citing works
Threats and Defenses in the Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Li, Y., Guo, Z., Yang, N., Chen, H., Yuan, D., & Ding, W. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3563537 · Cited by 38 · see citing works
Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT
Fan, M., Ji, K., Zhang, Z., Yu, H., & Sun, G. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3267112 · Cited by 37 · see citing works
A federated learning architecture for secure and private neuroimaging analysis
Stripelis, D., Gupta, U., Saleem, H., Dhinagar, N., Ghai, T., Anastasiou, C., Sánchez, R., Steeg, G. V., Ravi, S., Naveed, M., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101031 · Cited by 31 · see citing works
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Park, J., Samarakoon, S., Elgabli, A., Kim, J., Bennis, M., Kim, S.-L., & Debbah, M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3055679 · Cited by 25 · see citing works
Harnessing wireless channels for scalable and privacy-preserving federated learning
Elgabli, A., Park, J., Issaid, C. B., & Bennis, M. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3078783 · Cited by 14 · see citing works
- Authors
- Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., & Lam, K.-Y.
- Affiliations
- Nanyang Technological University (SG)
- Xi’an University of Posts and Telecommunications (CN)
- Ocean University of China (CN)
- National University of Singapore (SG)
- Venue
- IEEE Internet of Things Journal
- Published
- 2021-06-01
- Volume
- 8
- Issue
- 11
- Pages
- 8836-8853
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3106378891
- Cited by
- 456
- References
- 54
- Citations by year
- 2026: 40 · 2025: 121 · 2024: 103 · 2023: 84 · 2022: 66 · 2021: 36 · 2020: 6
- FWCI
- 18.86
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nanyang Technological University (NTU) Startup Grant
- Alibaba-NTU Singapore Joint Research Institute
- Singapore Ministry of Education Academic Research Fund (Tier 1 RG128/18, Tier 1 RG115/19, Tier 1 RT07/19, Tier 1 RT01/19, Tier 2 MOE2019-T2-1-176)
- NTU-WASP Joint Project
- Singapore National Research Foundation (NRF) under its Strategic Capability Research Centres Funding Initiative: Strategic Centre for Research in Privacy-Preserving Technologies and Systems
- Energy Research Institute @NTU
- Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE (DeST-SCI2019-0012, DeST-SCI2019-0007)
- AI Singapore 100 Experiments Programme
- NTU Project for Large Vertical Take-Off and Landing Research Platform
- National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative
- National Natural Science Foundation of China (61902365)
- China Postdoctoral Science Foundation (2019M652473)
- NRF, Singapore, under Singapore Energy Market Authority, Energy Resilience (NRF2017EWT-EP003-041, Singapore NRF2015-NRFISF001-2277)
- A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing (RGANS1906)
- Wallenberg AI, Autonomous Systems and Software Program and Nanyang Technological University (M4082187 (4080))
- Singapore Ministry of Education (MOE) Tier 1 (RG16/20)
- Alibaba Group through Alibaba Innovative Research Program
- Alibaba-NTU Singapore JRI
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- local differential privacy, federated learning, Internet of Vehicles, communication cost reduction, intelligent traffic management, privacy-utility trade-off
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
