Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets
Open accessAbstract
Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals’ unconditional consent or is often simply forbidden for privacy and security reasons. In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets. Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evaluation of machine-learning models on sensitive and distributed data. To ensure data confidentiality and the privacy of the data providers, Drynx combines interactive protocols, homomorphic encryption, zero-knowledge proofs of correctness, and differential privacy. It enables an efficient and decentralized verification of the input data and of all the system’s computations thus provides auditability in a strong adversarial model in which no entity has to be individually trusted. Drynx is highly modular, dynamic and parallelizable. Our evaluation shows that it enables the training of a logistic regression model on a dataset (12 features and 600,000 records) distributed among 12 data providers in less than 2 seconds. The computations are distributed among 6 computing nodes, and Drynx enables the verification of the query execution’s correctness in less than 22 seconds.
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?
Many state-of-the-art byzantine-robust aggregations rely on assumptions that are either unrealistic or do not hold in FL environments.
“Many of the state-of-the-art byzantine-robust aggregations rely on assumptions that are either unrealistic or do not hold in FL environments [18].”
From Vulnerabilities in federated learning (2021) · cites this paper as [18]
Cites
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075 · see citing works
Algorithm AS 136: A K-Means Clustering Algorithm
Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523 · see citing works
Boyd, S. (2010) · now publishers, Inc. eBooks · doi:10.1561/9781601984616 · Cited by 12,863 · see citing works
A public key cryptosystem and a signature scheme based on discrete logarithms
Elgamal, T. (1985) · IEEE Transactions on Information Theory · doi:10.1109/tit.1985.1057074 · Cited by 8,059 · 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
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
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
(2013) · Cited by 5,372 · see citing works
Dwork, C. (2006) · Lecture notes in computer science · doi:10.1007/11787006_1 · Cited by 5,362 · 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
Practical Byzantine fault tolerance
Castro, M., & Liskov, B. H. (1999) · Operating Systems Design and Implementation · doi:10.5555/296806.296824 · Cited by 4,026 · see citing works
How To Prove Yourself: Practical Solutions to Identification and Signature Problems
Fiat, A., & Adi Shamir, Z. (2007) · Springer eBooks · doi:10.1007/3-540-47721-7_12 · Cited by 3,972 · 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
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
Privacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356 · see citing works
SecureML: A System for Scalable Privacy-Preserving Machine Learning
Mohassel, P., & Zhang, Y. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.12 · Cited by 1,936 · see citing works
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
Phong, L. T., Aono, Y., Hayashi, T., Wang, L., & Moriai, S. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2787987 · Cited by 1,662 · see citing works
Blockchain distributed ledger technologies for biomedical and health care applications
Kuo, T.-T., Kim, H.-E., & Ohno-Machado, L. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx068 · Cited by 1,350 · see citing works
Popa, R. A., Redfield, C. M. S., Zeldovich, N., & Balakrishnan, H. (2011) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/2043556.2043566 · Cited by 1,166 · see citing works
Differentially Private Federated Learning: A Client Level Perspective
Geyer, R. C., Klein, T., & Nabi, M. (2017) · arXiv · doi:10.48550/arxiv.1712.07557 · Cited by 1,052 · see citing works
Pairing-Friendly Elliptic Curves of Prime Order
Barreto, P. S. L. M., & Naehrig, M. (2006) · Lecture notes in computer science · doi:10.1007/11693383_22 · Cited by 880 · see citing works
Sharemind: A Framework for Fast Privacy-Preserving Computations
Bogdanov, D., Laur, S., & Willemson, J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88313-5_13 · Cited by 698 · see citing works
Beal, A. C. (2012) · JAMA · doi:10.1001/jama.2012.500 · Cited by 625 · see citing works
High-speed high-security signatures
Bernstein, D. J., Duif, N., Lange, T., Schwabe, P., & Yang, B. (2012) · Journal of Cryptographic Engineering · doi:10.1007/s13389-012-0027-1 · Cited by 568 · see citing works
Enhancing Bitcoin Security and Performance with Strong Consistency via Collective SigningPreprint
Kokoris-Kogias, E., Jovanovic, P., Gailly, N., Khoffi, I., Gasser, L., & Ford, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.06997 · Cited by 565 · see citing works
Privacy-preserving logistic regression
Chaudhuri, K., & Monteleoni, C. (2008) · Cited by 526 · see citing works
A verifiable secret shuffle and its application to e-voting
Neff, C. A. (2001) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/501983.502000 · Cited by 513 · see citing works
Gazelle: A Low Latency Framework for Secure Neural Network InferencePreprint
Juvekar, C., Vaikuntanathan, V., & Chandrakasan, A. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.05507 · Cited by 503 · see citing works
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records
Nikolaenko, V., Weinsberg, U., Ioannidis, S., Jóye, M., Boneh, D., & Taft, N. (2013) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2013.30 · Cited by 447 · see citing works
Oblivious multi-party machine learning on trusted processors
Ohrimenko, O., Schuster, F., Cédric Fournet, A., Mehta, A., Nowozin, S., Vaswani, K., & Costa, M. (2016) · MPG.PuRe (Max Planck Society) · Cited by 402 · see citing works
How to Simulate It – A Tutorial on the Simulation Proof Technique
Lindell, Y. (2017) · Information security and cryptography · doi:10.1007/978-3-319-57048-8_6 · Cited by 382 · see citing works
Processing analytical queries over encrypted data
Tu, S., Kaashoek, M. F., Madden, S., & Zeldovich, N. (2013) · Proceedings of the VLDB Endowment · doi:10.14778/2535573.2488336 · Cited by 363 · see citing works
Dwork, C. (2011) · Springer eBooks · doi:10.1007/978-1-4419-5906-5_752 · Cited by 341 · see citing works
Centrally Banked Cryptocurrencies
Danezis, G., & Meiklejohn, S. (2016) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2016.23187 · Cited by 310 · see citing works
Efficient Protocols for Set Membership and Range Proofs
Camenisch, J. L., Chaabouni, R., & Shelat, A. A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89255-7_15 · Cited by 293 · see citing works
Mona: Secure Multi-Owner Data Sharing for Dynamic Groups in the Cloud
Liu, X., Zhang, Y., Wang, B., & Yan, J. (2012) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2012.331 · Cited by 275 · see citing works
Evaluating Differentially Private Machine Learning in Practice
Jayaraman, B., & Evans, D. (2019) · arXiv · doi:10.48550/arxiv.1902.08874 · Cited by 265 · see citing works
Towards practical differential privacy for SQL queries
Johnson, N., Near, J. P., & Song, D. X. (2018) · Proceedings of the VLDB Endowment · doi:10.1145/3187009.3177733 · Cited by 258 · see citing works
Universally Utility-maximizing Privacy Mechanisms
Ghosh, A., Roughgarden, T., & Sundararajan, M. (2012) · SIAM Journal on Computing · doi:10.1137/09076828x · Cited by 257 · see citing works
Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation
Kim, M., Song, Y., Wang, S., Xia, Y., & Jiang, X. (2018) · JMIR Medical Informatics · doi:10.2196/medinform.8805 · Cited by 257 · see citing works
Proof systems for general statements about discrete logarithms
Camenisch, J. L., & Stadler, M. A. (1997) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-006651937 · Cited by 221 · see citing works
DP-ADMM: ADMM-Based Distributed Learning With Differential Privacy
Huang, Z., Hu, R., Guo, Y., Chan-Tin, E., & Gong, Y. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2931068 · Cited by 221 · see citing works
Efficient Zero-Knowledge Argument for Correctness of a Shuffle
Bayer, S., & Groth, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29011-4_17 · Cited by 197 · see citing works
Privacy-preserving distributed linear regression on high-dimensional data
Gascón, A., Schoppmann, P., Balle, B., Raykova, M., Doerner, J., Zahur, S., & Evans, D. (2017) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2017-0053 · Cited by 193 · see citing works
Scalable and Secure Logistic Regression via Homomorphic Encryption
Aono, Y., Hayashi, T., Phong, L. T., & Wang, L. (2016) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/2857705.2857731 · Cited by 183 · see citing works
Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers
Pathak, M. A., Rane, S. D., & Raj, B. (2010) · Cited by 156 · 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
A Verifiable Secret Shuffe of Homomorphic Encryptions
Groth, J. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36288-6_11 · Cited by 147 · see citing works
Helen: Maliciously Secure Coopetitive Learning for Linear Models
Zheng, W., Popa, R. A., Gonzalez, J. E., & Stoica, I. G. (2019) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2019.00045 · Cited by 143 · see citing works
Bater, J., Elliott, G., Eggen, C., Goel, S., Kho, A. N., & Rogers, J. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3055330.3055334 · Cited by 136 · see citing works
Deriving genomic diagnoses without revealing patient genomes
Jagadeesh, K. A., Wu, D. J., Birgmeier, J. A., Boneh, D., & Bejerano, G. (2017) · Science · doi:10.1126/science.aam9710 · Cited by 134 · see citing works
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
Jayaraman, B., Wang, L., Evans, D., & Gu, Q. (2018) · neural information processing systems · Cited by 126 · see citing works
Towards statistical queries over distributed private user data
Chen, R., Reznichenko, A., Francis, P., & Gehrke, J. E. (2012) · Cited by 106 · see citing works
HotStuff: BFT Consensus in the Lens of BlockchainPreprint
Yin, M., Malkhi, D., Reiter, M. K., Gueta, G. G., & Abraham, I. (2018) · arXiv (Cornell University) · doi:10.4230/lipics.opodis.2022.18 · Cited by 104 · see citing works
Big Data Analytics over Encrypted Datasets with Seabed
Papadimitriou, A., Bhagwan, R., Chandran, N., Ramjee, R., Haberlen, A., Singh, H., Modi, A., & Badrinarayanan, S. (2016) · ScholarlyCommons (University of Pennsylvania) · Cited by 103 · see citing works
Achieving an effective, scalable and privacy-preserving data sharing service in cloud computing
Dong, X., Yu, J., Luo, Y., Chen, Y. J., Xue, G., & Li, M. (2014) · Computers & Security · doi:10.1016/j.cose.2013.12.002 · Cited by 99 · see citing works
MedCo: Enabling Secure and Privacy-Preserving Exploration of Distributed Clinical and Genomic Data
Raisaro, J. L., Troncoso-Pastoriza, J. R., Misbach, M., Sousa, J. S., Pradervand, S., Missiaglia, E., Michielin, O. A., Ford, B., & Hubaux, J. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2854776 · Cited by 98 · see citing works
Self-citationEfficient Private Statistics with Succinct Sketches
Melis, L., Danezis, G., & Cristofaro, E. D. (2016) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2016.23175 · Cited by 94 · see citing works
Privacy-Preserving Indexing and Query Processing for Secure Dynamic Cloud Storage
Du, M., Wang, Q., He, M., & Weng, J. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2818651 · Cited by 85 · see citing works
Private Information Retrieval for Secure Distributed Storage Systems
Yang, H., Shin, W., & Lee, J. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2833050 · Cited by 83 · see citing works
Nikitin, K., Kokoris-Kogias, E., Jovanovic, P., Gailly, N., Gasser, L., Khoffi, I., Cappos, J., & Ford, B. (2017) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 71 · see citing works
Secure and Differentially Private Logistic Regression for Horizontally Distributed Data
Kim, M., Lee, J., Lucila Ohno-machado, A., & Jiang, X. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2925496 · Cited by 69 · 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
Students and Taxes: a Privacy-Preserving Study Using Secure Computation
Bogdanov, D., Kamm, L., Kubo, B., Rebane, R., Sokk, V., & Talviste, R. (2016) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2016-0019 · Cited by 62 · see citing works
Verifiable Mixing (Shuing) of ElGamal Pairs
Ne, C. A. (2004) · Cited by 59 · see citing works
Differential privacy in telco big data platform
Hu, X., Yuan, M., Yao, J., Deng, Y., Chen, L., Yang, Q., Guan, H., & Zeng, J. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2824032.2824067 · Cited by 59 · see citing works
UnLynx: A Decentralized System for Privacy-Conscious Data Sharing
Froelicher, D., Egger, P., Sousa, J. S., Raisaro, J. L., Huang, Z., Mouchet, C., Ford, B., & Hubaux, J. (2017) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2017-0047 · Cited by 54 · see citing works
Self-citationScalable Anonymous Group Communication in the Anytrust Model
Wolinsky, D. I., Corrigan-Gibbs, H., Ford, B., & Johnson, A. (2012) · Cited by 34 · see citing works
Privacy, Fairness, and Respect for Individuals
Baker, D. B., Kaye, J. S., & Terry, S. F. (2016) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.13063/2327-9214.1207 · Cited by 30 · see citing works
Managing Identities Using Blockchains and CoSi
Kokoris-Kogias, L., Gasser, L., Khoffi, I., Jovanovic, P., Gailly, N., & Ford, B. (2016) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 22 · see citing works
High-Precision Privacy-Preserving Real-Valued Function Evaluation
Boura, C., Chillotti, I., Gama, N., Jetchev, D., Peceny, S., & Petric, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-662-58387-6_10 · Cited by 19 · see citing works
Lorgis, L., Zeller, M., Patrick Jourdain, M., Beaune, J., Cambou, J., Vaisse, B., Chamontin, B., & Cottin, Y. (2009) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2009.05.003 · Cited by 11 · see citing works
Flagged first, then by citations
5 of the 77 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Privacy‐preserving federated learning based on multi‐key homomorphic encryption
Ma, J., Naas, S., Sigg, S., & Lyu, X. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22818 · Cited by 458 · see citing works
Leveraging Data Science to Combat COVID-19: A Comprehensive Review
Latif, S., Usman, M., Manzoor, S., Iqbal, W., Qadir, J., Gareth Tyson, A., Castro, I., Razi, A., Boulos, M. N. K., Weller, A., et al. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2020.3020521 · Cited by 270 · see citing works
Vulnerabilities in federated learning
Bouacida, N., & Mohapatra, P. (2021) · IEEE Access · doi:10.1109/access.2021.3075203 · Cited by 248 · see citing works
Froelicher, D., Troncoso-Pastoriza, J. R., Raisaro, J. L., Cuendet, M. A., Sousa, J. S., Cho, H., Berger, B., Fellay, J., & Hubaux, J. (2021) · Nature Communications · doi:10.1038/s41467-021-25972-y · Cited by 208 · see citing works
Self-citationPOSEIDON: Privacy-Preserving Federated Neural Network Learning
Sav, S., Pyrgelis, A., Troncoso-Pastoriza, J. R., Froelicher, D., Bossuat, J.-P., Sousa, J. S., & Hubaux, J.-P. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24119 · Cited by 162 · see citing works
Self-citationDwivedi, A. D., Singh, R., Kaushik, K., Mukkamala, R. R., & Alnumay, W. S. (2021) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4329 · Cited by 160 · see citing works
Multiparty Homomorphic Encryption from Ring-Learning-with-Errors
Mouchet, C., Troncoso-Pastoriza, J. R., Bossuat, J.-P., & Hubaux, J. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2021-0071 · Cited by 141 · see citing works
Self-citationScheibner, J., Ienca, M., Kechagia, S., Troncoso-Pastoriza, J. R., Raisaro, J. L., Hubaux, J., Fellay, J., & Vayena, E. (2020) · Journal of Law and the Biosciences · doi:10.1093/jlb/lsaa010 · Cited by 90 · see citing works
Self-citationScalable 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 78 · see citing works
Self-citationXing, Z., Zhang, Z., Zhang, Z., Li, Z., Li, M., Liu, J., Zhang, Z., Zhao, Y., Sun, Q., Zhu, L., et al. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3561657 · Cited by 62 · see citing works
Lightweight Federated Learning for Large-Scale IoT Devices With Privacy Guarantee
Wei, Z., Pei, Q., Zhang, N., Liu, X., Wu, C., & Taherkordi, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3127886 · Cited by 43 · see citing works
Enabling secure time-series data sharing via homomorphic encryption in cloud-assisted IIoT
Halder, S., & Newe, T. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.03.032 · Cited by 37 · see citing works
μDFL: A Secure Microchained Decentralized Federated Learning Fabric Atop IoT Networks
Xu, R., & Chen, Y. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3179892 · Cited by 37 · see citing works
Scalable and Privacy-Preserving Federated Principal Component Analysis
Froelicher, D., Cho, H., Edupalli, M., Sousa, J. S., Bossuat, J.-P., Pyrgelis, A., Troncoso-Pastoriza, J. R., Berger, B., & Hubaux, J. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179350 · Cited by 31 · see citing works
Self-citationA survey of the fusion of traditional data security technology and blockchain
Xu, Y., Xu, G., Liu, Y., Liu, Y., & Shen, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124151 · Cited by 27 · see citing works
Privacy-preserving Federated Learning based on Multi-key Homomorphic EncryptionPreprint
Ma, J., Naas, S., Sigg, S., & Lyu, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.06824 · Cited by 21 · see citing works
Ballhausen, H., Corradini, S., Belka, C., Bogdanov, D., Boldrini, L., Bono, F., Goelz, C., Landry, G., Panza, G., Parodi, K., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01293-4 · Cited by 20 · see citing works
Niță, Ș. L., & Mihăilescu, M. I. (2023) · Springer eBooks · doi:10.1007/978-3-031-43214-9_3 · Cited by 18 · see citing works
Verifiable Data Mining Against Malicious Adversaries in Industrial Internet of Things
Ma, Z., Ma, J., Miao, Y., Liu, X., Choo, K. R., Gao, Y., & Deng, R. H. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3077005 · Cited by 16 · see citing works
Developing Privacy-preserving AI Systems: The Lessons learned
Chen, H., Hussain, S. U., Boemer, F., Stapf, E., Sadeghi, A. R., Koushanfar, F., & Cammarota, R. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218662 · Cited by 14 · see citing works
Privacy preservation techniques through data lifecycle: A comprehensive literature survey
Madhusudhanan, S., & Jose, A. C. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104473 · Cited by 14 · see citing works
Privacy-preserving framework for genomic computations via multi-key homomorphic encryption
Namazi, M., Farahpoor, M., Ayday, E., & Pérez‐González, F. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btae754 · Cited by 10 · see citing works
Private Analytics via Streaming, Sketching, and Silently Verifiable Proofs
Rathee, M., Zhang, Y., Corrigan-Gibbs, H., & Popa, R. A. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00245 · Cited by 8 · see citing works
Geometric Deep Lean Learning: Evaluation Using a Twitter Social Network
Villalba-Díez, J., Molina, M., & Schmidt, D. (2021) · Applied Sciences · doi:10.3390/app11156777 · Cited by 7 · see citing works
Practical Fully-Decentralized Secure Aggregation for Personal Data Management Systems
Mirval, J., Bouganim, L., & Sandu-Popa, I. (2021) · International Conference on Scientific and Statistical Database Management · doi:10.1145/3468791.3468821 · Cited by 6 · see citing works
Mirval, J., Bouganim, L., & Popa, I. S. (2023) · International Conference on Scientific and Statistical Database Management · doi:10.1145/3603719.3603730 · Cited by 6 · see citing works
Practical Security and Privacy for Database Systems
He, X. Q., Rogers, J., Bater, J., Machanavajjhala, A., Wang, C., & Wang, X. S. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457544 · Cited by 5 · see citing works
Veeraragavan, N. R., & Nygård, J. F. (2021) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3465481.3470071 · Cited by 5 · see citing works
A Blockchain-Based Data Sharing System with Enhanced Auditability
Xiao, Y., Xu, L., Chen, Z., Zhang, C., & Zhu, L. (2022) · Mathematics · doi:10.3390/math10234494 · Cited by 5 · see citing works
Jollès, E., Gillard, S., David, D. P., Strohmeier, M., & Mermoud, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35190-7_10 · Cited by 5 · see citing works
Privacy-Preserving Distributed Learning with Nonsmooth Objective Functions
Gauthier, F., Gratton, C., Venkategowda, N. K. D., & Werner, S. (2020) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf51394.2020.9443287 · Cited by 4 · see citing works
Data Privacy Preservation and Security Approaches for Sensitive Data in Big Data
Nikam, R. R., & Shahapurkar, R. (2021) · Advances in parallel computing · doi:10.3233/apc210221 · Cited by 4 · see citing works
HEND-FL: Accurate Federated Learning Using Homomorphic Encryption and a New Distributed Protocol
Zhou, D., Chen, Z., Wu, D., Yu, Y., Gan, Q., & Xu, B. (2022) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cy55231.2022.9928012 · Cited by 3 · see citing works
Privacy-protected aggregation in federated learning based on semi-homomorphic encryption
Lin, H., Chen, C., & Hu, Y. (2023) · International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) · doi:10.1117/12.2685483 · Cited by 3 · see citing works
The Combination of AI, Blockchain, and the Internet of Things for Patient Relationship Management
Nacer, M. I., Prakoonwit, S., & Alarab, I. (2021) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-030-70478-0_3 · Cited by 2 · see citing works
Secure Data Aggregation in Federated Learning
Yu, S., & Cui, L. (2022) · Digital Privacy and Security · doi:10.1007/978-981-19-8692-5_7 · Cited by 2 · see citing works
CanDIG: Secure Federated Genomic Queries and Analyses Across JurisdictionsPreprint
Dursi, L. J., Bozóky, Z., de Borja, R., Li, J., Bujold, D., Lipski, A., Rashid, S. F., Sethi, A., Memon, N., Naidoo, D., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.30.434101 · Cited by 2 · see citing works
2020 Index IEEE Transactions on Information Forensics and Security Vol. 15
(2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3053735 · Cited by 2 · see citing works
Homomorphic encryption-based ciphertext anomaly detection method for e-health records
李, 腾., 方, 保., 马, 卓., 沈, 玉., & 马, 建. (2022) · Scientia Sinica Informationis · doi:10.1360/ssi-2022-0214 · Cited by 1 · see citing works
Xu, Y., Zhang, J., Dong, X., Wang, A., Wang, X., & Zhu, L. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3608005 · Cited by 1 · see citing works
Yang, Y., Zhang, L., Zhao, Y., & Choo, K. R. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3628257 · Cited by 1 · see citing works
Troncoso-Pastoriza, J. R., Froelicher, D., Hu, P., Aloufi, A., & Hubaux, J. (2021) · Springer eBooks · doi:10.1007/978-3-030-77287-1_3 · Cited by 1 · see citing works
Self-citationHomomorphic Encryption-Friendly Privacy-Preserving Partitioning Algorithm for Differential Privacy
Ushiyama, S., Takahashi, T., Kudo, M., & Yamana, H. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020699 · Cited by 1 · see citing works
Research progress of verifiable technologies for outsourcing services
Yi, S. U. N., Fan, Y. A. N. G., Xingyuan, C. H. E. N., Du, X., & Wei, L. I. N. (2023) · Scientia Sinica Informationis · doi:10.1360/ssi-2022-0360 · Cited by 1 · see citing works
PRIDA: PRIvacy-Preserving Data Aggregation with Multiple Data Customers
Bozdemir, B., Özdemir, B. A., & Önen, M. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-65175-5_4 · Cited by 1 · see citing works
Succinct Hash-Based Arbitrary-Range Proofs
Li, W., Zhang, Z., Guo, Y., Chow, S. S. M., & Wan, Z. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3497806 · Cited by 1 · see citing works
Privacy-Preserving Ridge Regression Over Encrypted Data Under Multiple Keys
Li, Y., 赖俊祚, Sun, M., Song, B., & Deng, R. H. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3554563 · Cited by 1 · see citing works
Poisoning Attacks and Counterattacks in Federated Learning
Yu, S., & Cui, L. (2022) · Digital Privacy and Security · doi:10.1007/978-981-19-8692-5_3 · Cited by 1 · see citing works
A Distributed Computing Solution for Privacy-Preserving Genome-Wide Association StudiesPreprint
Brito, C. V., Ferreira, P. G., & Paulo, J. T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.15.575678 · Cited by 1 · see citing works
Nguyen, T., Asghar, H. J., Bhakar, R., Kaafar, D., & Farokhi, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09904
Edgelet computing: enabling privacy-preserving decentralized data processing at the network edge
Javet, L., Anciaux, N., Bouganim, L., & Pucheral, P. (2024) · Personal and Ubiquitous Computing · doi:10.1007/s00779-024-01821-9
Hosen, M. N., Billah, M. M., & Uddin, R. (2024) · European Journal of Theoretical and Applied Sciences · doi:10.59324/ejtas.2024.2(5).64
Exploiting Trusted Execution Environments and Distributed Computation for Genomic Association Tests
Brito, C. V., Ferreira, P. G., & Paulo, J. T. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3562364
Advancements in Privacy-Preserving Techniques for Patient Data Protection
Mohile, K. (2025) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2025.7.3.64
Federated Learning Privacy-preserving Scheme Based on DMK-CKKS
Zhao, Y., Li, H., Wang, W., Liu, Y., & Zou, C. (2025) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit65432.2025.11035269
Revisiting Module Lattice-based Homomorphic Encryption and Application to Secure-MPC
Mukherjee, A., & Roy, S. S. (2025) · IACR Communications in Cryptology · doi:10.62056/ay7qjp10
Safavi, S., Abdulnabi, M. S. H., & Alizadeh, S. (2025) · International Conference on Metaverse and Current Trends in Computing (ICMCTC) · doi:10.1109/icmctc62214.2025.11196213
SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacy
Capano, F., Böhler, J., & Weggenmann, B. (2026) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml68715.2026.00057
- Authors
- Froelicher, D., Troncoso-Pastoriza, J. R., Sousa, J. S., & Hubaux, J.-P.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2020
- Volume
- 15
- Pages
- 3035-3050
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2934042028
- Cited by
- 59
- References
- 77
- Citations by year
- 2026: 1 · 2025: 11 · 2024: 8 · 2023: 6 · 2022: 9 · 2021: 19 · 2020: 5
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Strategic Focal Area Personalized Health and Related Technologies (PHRT) of the ETH Domain (2017-201)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- privacy-preserving data sharing, decentralized computing, distributed datasets, homomorphic encryption, zero-knowledge proofs, differential privacy, interactive protocols, verifiable computation, auditability, statistical queries, data confidentiality
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
