The DOI matches its Crossref record; 43 of the 50 references it cites were found in a registry or an index.
Prochlo: Strong Privacy for Analytics in the Crowd
Open accessAbstract
The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring.
Overview
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
Verification
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?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Hamer et al. [219] proposed a communication-efficient federated algorithm for learning mixture weights on an ensemble of pre-trained models, based on communicating only a subset of the models to any one device;”
From Advances and Open Problems in Federated Learning (2021) · cites this paper as [219]
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.
Citations
Cites
Shamir, A. (1979) · Communications of the ACM · doi:10.1145/359168.359176 · Cited by 13,756 · cited 1×
“First, recall the basic idea behind Shamir secret sharing [68]: t-secret sharing splits a secret s in a field F into arbitrary shares s1, s2, .”
BackgroundCalibrating 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
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 · cited 2×
“These settings are at least as strong as in recent industrial deployments and in literature [4, 28, 70].”
BackgroundSelf-citationThe 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 · cited 5×
“The analyzer, which has the most complete view of the reported data, may choose from a wide variety of differentially private mechanisms and data processing systems [27,31,38, 53, 63].”
BackgroundMethodInfluentialDouceur, J. R. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45748-8_24 · Cited by 4,419
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956 · cited 1×
“Other cryptography-based privacy-protection systems, such as PDDP, Prio, and Secure Aggregation [11, 15, 18], mostly share ESA’s goals but differ greatly in their approach.”
BackgroundL -diversity: Privacy beyond k -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 · cited 3×
“iduals [65,66]. However, since k-anonymity does not limit statistical inferences drawn indirectly, from subgroup intersections or differences, it cannot offer mathematically-strong privacy guarantees [32,48]. In the last decade, the definitions of differential privacy have become the accepted standard for strong privac…”
BackgroundMethodInfluentialSorting networks and their applications
Batcher, K. E. (1968) · doi:10.1145/1468075.1468121 · Cited by 2,442 · cited 1×
“Batcher’s sort is one such algorithm [8].”
Robust De-anonymization of Large Sparse Datasets
Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372 · cited 1×
“Famously, Narayanan and Shmatikov [54] managed to deanonymize users in the challenge’s large corpus of training data by exploiting auxiliary data and the linkability of users’ movie preferences.”
BackgroundProtecting respondents identities in microdata release
Samarati, P. (2001) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.971193 · Cited by 2,339 · cited 1×
“For example, the well-known property of k-anonymity prevents direct release of information about subgroups with fewer than k individuals [65, 66].”
Background
Flagged first, then by citations
7 of the 50 references couldn’t be matched to a known paper.
Research works printed but not linked: 34
- Abadi , M. , Barham , P. , Chen , J. , Chen , Z. , Davis , A. , Dean , J. , Devin , M. , Ghemawat , S. , Irving , G. , Isard , M. , Kudlur , M. , Levenberg , J. , Monga , R. , Moore , S. , Murray , D. G. , Steiner , B. , Tucker , P. , Vasudevan , V. , Warden , P. , Wicke , M. , Yu , Y. , and Zheng , X . Tensorflow: A system for large-scale machine learning . In Proc. of 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI) ( 2016 ), pp. 265 -- 283 . Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X. Tensorflow: A system for large-scale machine learning. In Proc. of 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2016), pp. 265--283.
- Abadi , M. , Boneh , D. , Mironov , I. , Raghunathan , A. , and Segev , G . Message-Locked Encryption for Lock-Dependent Messages. In Advances in Cryptology--CRYPTO ( 2013 ), pp. 374 -- 391 . Abadi, M., Boneh, D., Mironov, I., Raghunathan, A., and Segev, G. Message-Locked Encryption for Lock-Dependent Messages. In Advances in Cryptology--CRYPTO (2013), pp. 374--391.
- Abadi , M. , Erlingsson , Ú., Goodfellow , I. , McMahan , H. B. , Papernot , N. , Mironov , I. , Talwar , K. , and Zhang , L . On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. In Proc. of IEEE 30th Computer Security Foundations Symposium (CSF) ( 2017 ), pp. 1 -- 6 . Abadi, M., Erlingsson, Ú., Goodfellow, I., McMahan, H. B., Papernot, N., Mironov, I., Talwar, K., and Zhang, L. On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. In Proc. of IEEE 30th Computer Security Foundations Symposium (CSF) (2017), pp. 1--6.
- Avent , B. , Korolova , A. , Zeber , D. , Hovden , T. , and Livshits , B . BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model . In Proc. of the 26th USENIX Security Symposium ( 2017 ), pp. 747 -- 764 . Avent, B., Korolova, A., Zeber, D., Hovden, T., and Livshits, B. BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model. In Proc. of the 26th USENIX Security Symposium (2017), pp. 747--764.
- Bassily , R. , Nissim , K. , Stemmer , U. , and Thakurta , A . Practical Locally Private Heavy Hitters. CoRR abs/1707.0498 ( 2017 ). http://arxiv.org/abs/1707.04982. Bassily, R., Nissim, K., Stemmer, U., and Thakurta, A. Practical Locally Private Heavy Hitters. CoRR abs/1707.0498 (2017). http://arxiv.org/abs/1707.04982. · arXiv
- Bellare , M. , Keelveedhi , S. , and Ristenpart , T . Message-Locked Encryption and Secure Deduplication. In Advances in Cryptology---EUROCRYPT ( 2013 ), pp. 296 -- 312 . Bellare, M., Keelveedhi, S., and Ristenpart, T. Message-Locked Encryption and Secure Deduplication. In Advances in Cryptology---EUROCRYPT (2013), pp. 296--312.
- Bulck , J. V. , Weichbrodt , N. , Kapitza , R. , Piessens , F. , and Strackx , R . Telling Your Secrets without Page Faults: Stealthy Page Table-Based Attacks on Enclaved Execution . In Proc. of 26th USENIX Security Symposium ( 2017 ), pp. 1041 -- 1056 . Bulck, J. V., Weichbrodt, N., Kapitza, R., Piessens, F., and Strackx, R. Telling Your Secrets without Page Faults: Stealthy Page Table-Based Attacks on Enclaved Execution. In Proc. of 26th USENIX Security Symposium (2017), pp. 1041--1056.
- Buse , R. P. L. , and Zimmermann , T . Information Needs for Software Development Analytics. In Proc. of the 34th International Conference on Software Engineering (ICSE) ( 2012 ), pp. 987 -- 996 . Buse, R. P. L., and Zimmermann, T. Information Needs for Software Development Analytics. In Proc. of the 34th International Conference on Software Engineering (ICSE) (2012), pp. 987--996.
- Chen , R. , Reznichenko , A. , Francis , P. , and Gehrke , J . Towards Statistical Queries over Distributed Private User Data . In Proc. of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI) ( 2012 ), pp. 169 -- 182 . Chen, R., Reznichenko, A., Francis, P., and Gehrke, J. Towards Statistical Queries over Distributed Private User Data. In Proc. of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI) (2012), pp. 169--182.
- Chromium Projects . RAPPOR (Randomized Aggregatable Privacy Preserving Ordinal Responses). https://www.chromium.org/developers/design-documents/rappor , 2017 . Chromium Projects. RAPPOR (Randomized Aggregatable Privacy Preserving Ordinal Responses). https://www.chromium.org/developers/design-documents/rappor, 2017.
- Corrigan-Gibbs , H. , and Boneh , D . Prio: Private, Robust, and Scalable Computation of Aggregate Statistics . In Proc. of the 14th USENIX Symposium on Networked Systems Design and Implementation (NDSI) ( 2017 ), pp. 259 -- 282 . Corrigan-Gibbs, H., and Boneh, D. Prio: Private, Robust, and Scalable Computation of Aggregate Statistics. In Proc. of the 14th USENIX Symposium on Networked Systems Design and Implementation (NDSI) (2017), pp. 259--282.
- Dang , H. , Dinh , T. T. A. , Chang , E.-C. , and Ooi , B. C. Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute. Proceedings on Privacy Enhancing Technologies , 3 ( 2017 ), 18 -- 35 . Dang, H., Dinh, T. T. A., Chang, E.-C., and Ooi, B. C. Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute. Proceedings on Privacy Enhancing Technologies, 3 (2017), 18--35.
- Denning , D. E. R. Cryptography and Data Security . Addison-Wesley , Boston, MA, USA , 1982 . Denning, D. E. R. Cryptography and Data Security. Addison-Wesley, Boston, MA, USA, 1982.
- Dinh , T. T. A. , Saxena , P. , Chang , E.-C. , Ooi , B. C. , and Zhang , C . M2R: Enabling Stronger Privacy in MapReduce Computation . In Proc. of the 24th USENIX Security Symposium ( 2015 ), pp. 447 -- 462 . Dinh, T. T. A., Saxena, P., Chang, E.-C., Ooi, B. C., and Zhang, C. M2R: Enabling Stronger Privacy in MapReduce Computation. In Proc. of the 24th USENIX Security Symposium (2015), pp. 447--462.
- 10.5555/646334.687813 · DOI
- Dwork , C. Differential Privacy: A Survey of Results. In International Conference on Theory and Applications of Models of Computation (TAMC) ( 2008 ), pp. 1 -- 19 . Dwork, C. Differential Privacy: A Survey of Results. In International Conference on Theory and Applications of Models of Computation (TAMC) (2008), pp. 1--19.
- Gaboardi , M. , Honaker , J. , King , G. , Nissim , K. , Ullman , J. , and Vadhan , S. P . PSI (Ψ): A Private data Sharing Interface. CoRR abs/1609.04340 ( 2016 ). http://arxiv.org/abs/1609.04340. Gaboardi, M., Honaker, J., King, G., Nissim, K., Ullman, J., and Vadhan, S. P. PSI (Ψ): A Private data Sharing Interface. CoRR abs/1609.04340 (2016). http://arxiv.org/abs/1609.04340. · arXiv
- Intel® Software Guard Extensions Programming Reference. https://software.intel.com/sites/default/files/managed/48/88/329298-002.pdf , Oct. 2014 . Intel® Software Guard Extensions Programming Reference. https://software.intel.com/sites/default/files/managed/48/88/329298-002.pdf, Oct. 2014.
- Intel® Software Guard Extensions (Intel® SGX) SDK . https://software.intel.com/en-us/sgx-sdk , 2017 . Intel® Software Guard Extensions (Intel® SGX) SDK. https://software.intel.com/en-us/sgx-sdk, 2017.
- Johnson , N. , Near , J. P. , and Song , D . Towards Practical Differential Privacy for SQL Queries. CoRR abs/1706.09479 ( 2017 ). https://arxiv.org/abs/1706.09479. Johnson, N., Near, J. P., and Song, D. Towards Practical Differential Privacy for SQL Queries. CoRR abs/1706.09479 (2017). https://arxiv.org/abs/1706.09479. · arXiv
- Lee , S. , Shih , M.-W. , Gera , P. , Kim , T. , Kim , H. , and Peinado , M . Inferring Fine-grained Control Flow Inside SGX Enclaves with Branch Shadowing. In Proc. of the 26th USENIX Security Symposium ( 2017 ), pp. 557 -- 574 . Lee, S., Shih, M.-W., Gera, P., Kim, T., Kim, H., and Peinado, M. Inferring Fine-grained Control Flow Inside SGX Enclaves with Branch Shadowing. In Proc. of the 26th USENIX Security Symposium (2017), pp. 557--574.
- Maniatis , P. , and Baker , M . Secure History Preservation Through Timeline Entanglement. In Proc. of the 11th USENIX Security Symposium ( 2002 ), pp. 297 -- 312 . Maniatis, P., and Baker, M. Secure History Preservation Through Timeline Entanglement. In Proc. of the 11th USENIX Security Symposium (2002), pp. 297--312.
- Maniatis , P. , Mironov , I. , and Talwar , K . Oblivious Stash Shuffle. CoRR abs/1709.07553 ( 2017 ). https://arxiv.org/abs/1709.07553. Maniatis, P., Mironov, I., and Talwar, K. Oblivious Stash Shuffle. CoRR abs/1709.07553 (2017). https://arxiv.org/abs/1709.07553. · arXiv
- Ohrimenko , O. , Goodrich , M. T. , Tamassia , R. , and Upfal , E . The Melbourne Shuffle: Improving Oblivious Storage in the Cloud. In Automata, Languages, and Programming : 41st International Colloquium, ICALP 2014 , Part II ( 2014 ), pp. 556 -- 567 . Ohrimenko, O., Goodrich, M. T., Tamassia, R., and Upfal, E. The Melbourne Shuffle: Improving Oblivious Storage in the Cloud. In Automata, Languages, and Programming: 41st International Colloquium, ICALP 2014, Part II (2014), pp. 556--567.
- Ravindranath , L. , Padhye , J. , Agarwal , S. , Mahajan , R. , Obermiller , I. , and Shayandeh , S . AppInsight: Mobile App Performance Monitoring in the Wild. In Proc. of the 10th USENIX Conference on Operating Systems Design and Implementation (OSDI) ( 2012 ), pp. 107 -- 120 . Ravindranath, L., Padhye, J., Agarwal, S., Mahajan, R., Obermiller, I., and Shayandeh, S. AppInsight: Mobile App Performance Monitoring in the Wild. In Proc. of the 10th USENIX Conference on Operating Systems Design and Implementation (OSDI) (2012), pp. 107--120.
- Roy , I. , Setty , S. T. V. , Kilzer , A. , Shmatikov , V. , and Witchel , E . Airavat: Security and Privacy for MapReduce. In Proc. of the 7th USENIX Symposium on Networked Systems Design and Implementation (NSDI) ( 2010 ), pp. 297 -- 312 . Roy, I., Setty, S. T. V., Kilzer, A., Shmatikov, V., and Witchel, E. Airavat: Security and Privacy for MapReduce. In Proc. of the 7th USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2010), pp. 297--312.
- Saltzer , J. H. , and Schroeder , M. D. The Protection of Information in Computer Systems. Proc. of the IEEE 63 , 9 ( 1975 ), 1278--1308. Saltzer, J. H., and Schroeder, M. D. The Protection of Information in Computer Systems. Proc. of the IEEE 63, 9 (1975), 1278--1308.
- Tang , J. , Korolova , A. , Bai , X. , Wang , X. , and Wang , X . Privacy Loss in Apple's Implementation of Differential Privacy on macOS 10.12. arXiv:1709.02753 ( 2017 ). https://arxiv.org/abs/1709.02753. Tang, J., Korolova, A., Bai, X., Wang, X., and Wang, X. Privacy Loss in Apple's Implementation of Differential Privacy on macOS 10.12. arXiv:1709.02753 (2017). https://arxiv.org/abs/1709.02753. · arXiv
- Vallina-Rodriguez , N. , Sundaresan , S. , Razaghpanah , A. , Nithyanand , R. , Allman , M. , Kreibich , C. , and Gill , P . Tracking the Trackers: Towards Understanding the Mobile Advertising and Tracking Ecosystem. CoRR abs/1609.07190 ( 2016 ). http://arxiv.org/abs/1609.07190. Vallina-Rodriguez, N., Sundaresan, S., Razaghpanah, A., Nithyanand, R., Allman, M., Kreibich, C., and Gill, P. Tracking the Trackers: Towards Understanding the Mobile Advertising and Tracking Ecosystem. CoRR abs/1609.07190 (2016). http://arxiv.org/abs/1609.07190. · arXiv
- Viega , J. , Chandra , P. , and Messier , M . Network Security with OpenSSL , 1 st ed. O'Reilly & Associates, Inc. , Sebastopol, CA, USA , 2002 . Viega, J., Chandra, P., and Messier, M. Network Security with OpenSSL, 1st ed. O'Reilly & Associates, Inc., Sebastopol, CA, USA, 2002.
- Wang , T. , Blocki , J. , Li , N. , and Jha , S . Locally Differentially Private Protocols for Frequency Estimation. In Proc. of the 26th USENIX Security Symposium ( 2017 ), pp. 729 -- 745 . Wang, T., Blocki, J., Li, N., and Jha, S. Locally Differentially Private Protocols for Frequency Estimation. In Proc. of the 26th USENIX Security Symposium (2017), pp. 729--745.
- Warner , S. L. Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias. J. of the American Statistical Association 60 , 309 ( 1965 ), 63--69. Warner, S. L. Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias. J. of the American Statistical Association 60, 309 (1965), 63--69.
- 10.5555/2555692.2555725 · DOI
- Zheng , W. , Dave , A. , Beekman , J. G. , Popa , R. A. , Gonzalez , J. E. , and Stoica , I . Opaque: An Oblivious and Encrypted Distributed Analytics Platform. In Proc. of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI) ( 2017 ), pp. 283 -- 298 . Zheng, W., Dave, A., Beekman, J. G., Popa, R. A., Gonzalez, J. E., and Stoica, I. Opaque: An Oblivious and Encrypted Distributed Analytics Platform. In Proc. of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2017), pp. 283--298.
Cited by
Sorted by citations
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
“Distributed DP via secure shuffling Another distributed differential privacy model is the shuffling model, which was kicked off by the recently introduced Encode-Shuffle-Analyze (ESA) framework [67] (illustrated in Figure 3).”
BackgroundMethodInfluentialSecure Single-Server Aggregation with (Poly)Logarithmic Overhead
Bell, J., Bonawitz, K., Gascón, A., Lepoint, T., & Raykova, M. (2020) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3372297.3417885 · Cited by 498
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 420
“[6] evaluate real-world implementations of LDP.”
BackgroundAmplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., & Thakurta, A. (2019) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611975482.151 · Cited by 325
Self-citationDistributed Differential Privacy via Shuffling
Cheu, A., Smith, A., Ullman, J., Zeber, D., & Zhilyaev, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-17653-2_13 · Cited by 268
“[20] and the discussion in [5]).”
BackgroundMethodInfluentialOort: Efficient Federated Learning via Guided Participant SelectionPreprint
Lai, F., Zhu, X., Madhyastha, H. V., & Chowdhury, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06081 · Cited by 262
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy
Sun, L., Qian, J., & Chen, X. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/217 · Cited by 267
“Previous efforts [Bittau et al., 2017; Erlingsson et al., 2019] have shown that when data reports are properly anonymized at each timestep and unlinkable over the time, the overall privacy guarantee can be greatly improved.”
ResultThe Privacy Blanket of the Shuffle Model
Balle, B., Bell, J., Gascón, A., & Nissim, K. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-26951-7_22 · Cited by 214
Privacy-first health research with federated learning
Sadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., Ingerman, A., Mellem, S., Kairouz, P., Nsoesie, E. O., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00489-2 · Cited by 226
Locally Differentially Private Frequent Itemset Mining
Wang, T., Li, N., & Jha, S. (2018) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2018.00035 · Cited by 206
PrivKV: Key-Value Data Collection with Local Differential Privacy
Ye, Q., Hu, H., Meng, X., & Zheng, H. (2019) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2019.00018 · Cited by 187
“For privacy-preserving system architecture, Prio [13] assumes at least one trusted server in a multi-server setup, whereas Prochlo [7] can support various levels of trust.”
BackgroundFederated Learning with Buffered Asynchronous AggregationPreprint
Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., Rabbat, M., Esmaeili, M. M., & Huba, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06639 · Cited by 182
“LDP for high dimensional data has been criticized for poor privacy-utility trade-off (Erlingsson et al., 2020; Bittau et al., 2017).”
BackgroundHow to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Ponomareva, N., Hazimeh, H., Kurakin, A., Xu, Z., Denison, C., McMahan, H. B., Vassilvitskii, S., Chien, S., & Thakurta, A. G. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14649 · Cited by 166
“Distributed DP*. Distributed DP seeks to recover the utility of central DP without having to rely on a fully trusted central aggregator (Dwork et al., 2006a; Bittau et al., 2017; Kairouz et al., 2021b; Agarwal et al., 2021).”
BackgroundHelen: 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 150
Aziz, R., Banerjee, S., Bouzefrane, S., & Vinh, T. L. (2023) · Future Internet · doi:10.3390/fi15090310 · Cited by 149
Challenges and future directions of secure federated learning: a survey
Zhang, K., Song, X., Zhang, C., & Yu, S. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0598-z · Cited by 127
Eskandarian, S., & Zaharia, M. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3364324.3364331 · Cited by 134
Volgushev, N., Schwarzkopf, M., Getchell, B., Varia, M., Lapets, A., & Azer Bestavros, C. (2019) · European Conference on Computer Systems · doi:10.1145/3302424.3303982 · Cited by 123
The distributed discrete gaussian mechanism for federated learning with secure aggregation
Kairouz, P., Liu, Z., & Steinke, T. (2021) · arXiv · doi:10.48550/arxiv.2102.06387 · Cited by 117
“FL with SecAgg and Distributed DP Despite the recent surge of interest in distributed DP, much of the work in this space focuses on the shuffled model of DP where a trusted third party (or a trusted execution environment) shuffles the noisy client updates before forwarding them to the server (Erlingsson et al., 2019;…”
BackgroundKotsogiannis, I., Tao, Y., He, X., Fanaeepour, M., Machanavajjhala, A., Hay, M., & Miklau, G. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3342263.3342274 · Cited by 113
“Recently, we have seen several real-world deployments at Google [8, 16], Apple [11], and the US Census Bureau [2,19,30].”
BackgroundManipulation Attacks in Local Differential Privacy
Cheu, A., Smith, A., & Ullman, J. (2021) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp40001.2021.00001 · Cited by 132
FLAME: Differentially Private Federated Learning in the Shuffle Model
Liu, R., Cao, Y., Chen, H., Guo, R., & Yoshikawa, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17053 · Cited by 111
A Comprehensive Survey on Local Differential Privacy
Xiong, X., Liu, S., Li, D., Cai, Z., & Niu, X. (2020) · Security and Communication Networks · doi:10.1155/2020/8829523 · Cited by 110
“Shuttle model [85] is a novel augmented LDP model for guaranteeing the data and identity privacy of participants.”
BackgroundMethodInfluentialDifferentially Private Byzantine-Robust Federated Learning
Ma, X., Sun, X., Wu, Y., Liu, Z., Chen, X., & Dong, C. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3167434 · Cited by 94
Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges
Ulukuş, Ş., Avestimehr, S., Gastpar, M., Jafar, S. A., Tandon, R., & Tian, C. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3142358 · Cited by 92
“Works that consider shuffling as part of the pipeline include [167], [168].”
BackgroundFoundation Models and Fair UsePreprint
Henderson, P., Li, X., Jurafsky, D., Hashimoto, T., Lemley, M. A., & Liang, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4404340 · Cited by 179
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
Feldman, V., McMillan, A., & Talwar, K. (2022) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs52979.2021.00096 · Cited by 122
The 2020 Census Disclosure Avoidance System TopDown Algorithm
Abowd, J. M., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., et al. (2022) · Harvard Data Science Review · doi:10.1162/99608f92.529e3cb9 · Cited by 113
Private Summation in the Multi-Message Shuffle Model
Balle, B., Bell, J., Gascón, A., & Kobbi Nissim, B. (2020) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3372297.3417242 · Cited by 97
Forecasting influenza activity using machine-learned mobility map
Venkatramanan, S., Sadilek, A., Fadikar, A., Barrett, C. L., Biggerstaff, M., Chen, J., Dotiwalla, X., Eastham, P., Gipson, B. R., Higdon, D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21018-5 · Cited by 86
Mireshghallah, F., Taram, M., Ramrakhyani, P., Jalali, A., Tullsen, D. M., & Esmaeilzadeh, H. (2020) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3373376.3378522 · Cited by 92
“In a more practical model, called local differential privacy, the system does not require users to even trust the curator to inspect their data, even for the purpose of preserving privacy [14, 15, 73, 74].”
BackgroundHaghani, M., Coughlan, M., Crabb, B., Dierickx, A., Feliciani, C., van Gelder, R., Geoerg, P., Hocaoglu, N., Laws, S., Lovreglio, R., et al. (2023) · Safety Science · doi:10.1016/j.ssci.2023.106292 · Cited by 94
“However, it is equally important to protect the privacy of individuals who are being monitored (Bittau et al., 2017; Chan et al., 2008; Stanciu et al., 2021).”
Federated Learning and Privacy
Bonawitz, K., Kairouz, P., McMahan, H. B., & Ramage, D. (2021) · Queue · doi:10.1145/3494834.3500240 · Cited by 76
Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey
Husnoo, M. A., Anwar, A., Chakrabortty, R. K., Doss, R., & Ryan, M. J. (2021) · IEEE Access · doi:10.1109/access.2021.3124309 · Cited by 74
Local Differential Privacy for Evolving Data
Joseph, M., Roth, A., Ullman, J., & Waggoner, B. (2020) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.718 · Cited by 75
SGX-LKL: Securing the Host OS Interface for Trusted ExecutionPreprint
Priebe, C., Muthukumaran, D., Lind, J., Zhu, H., Cui, S., Sartakov, V. A., & Pietzuch, P. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11143 · Cited by 96
The Internet of Federated Things (IoFT)
Kontar, R. A., Shi, N., Yue, X., Chung, S., Byon, E., Chowdhury, M., Jin, J. J., Kontar, W., Masoud, N., Nouiehed, M., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3127448 · Cited by 72
“As a result, secure computation, aggregation and communication are needed in IoFT [17, 23].”
BackgroundYao, A., Pal, S., Li, G., Li, X., Zhang, Z., Jiang, F., Dong, C., Jia, X., & Liu, X. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107706 · Cited by 70
Efficient federated learning privacy preservation method with heterogeneous differential privacy
Ling, J., Zheng, J., & Chen, J. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103715 · Cited by 64
Federated Learning: A Distributed Shared Machine Learning Method
Hu, K., Li, Y., Xia, M., Wu, J., Lu, M., Zhang, S., & Weng, L. (2021) · Complexity · doi:10.1155/2021/8261663 · Cited by 69
“Some researchers have proposed secure mixing [74] and secure mixing [75] methods, but this increases the cost of communication.”
BackgroundScalable and Differentially Private Distributed Aggregation in the Shuffled Model
Ghazi, B., Pagh, R., & Velingker, A. (2019) · arXiv · doi:10.48550/arxiv.1906.08320 · Cited by 62
Federated Heavy Hitters Discovery with Differential PrivacyPreprint
Zhu, W., Kairouz, P., McMahan, H. B., Sun, H., & Li, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08534 · Cited by 66
“Another approach is to use the ESA architecture of [7] to ensure shuffling and anonymization of the votes.”
BackgroundMethodAggregating Crowd Wisdom via Blockchain: A Private, Correct, and Robust Realization
Duan, H., Zheng, Y., Du, Y., Zhou, A., Wang, C., & Au, M. H. (2019) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2019.8767412 · Cited by 59
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
“g, secure aggregation [2] or secure shuffling [1].”
BackgroundBichsel, B., Gehr, T., Drachsler-Cohen, D., Tsankov, P., & Vechev, M. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243863 · Cited by 57
“3243863 1 INTRODUCTION Differential privacy (DP) [12] has emerged as an important property that measures the amount of leaked information by (randomized) algorithms [2, 7, 14, 21, 22, 24, 27, 29, 31].”
BackgroundPrivacy-preserving Data Mining in Industry
Kenthapadi, K., Mironov, I., & Thakurta, A. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291384 · Cited by 58
Self-citationAhmad, I., Shahabuddin, S., Sauter, T., Harjula, E., Kumar, T., Meisel, M., Juntti, M. J., & Ylianttila, M. (2020) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2020.2979272 · Cited by 58
“There are various approaches now emerging to safeguard the privacy of user data, such as differential privacy models [44] and the encoding and shuffling of algorithms [48].”
MethodThe Role of Interactivity in Local Differential Privacy
Joseph, M., Mao, J., Neel, S., & Roth, A. (2019) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2019.00015 · Cited by 61
“In the last several years, differential privacy in the local model has seen wide adoption in industry, including at Google [6, 19], Apple [3], and Microsoft [14].”
BackgroundBao, E., Yang, Y., Xiao, X., & Ding, B. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3476249.3476277 · Cited by 47
Fidelius: Protecting User Secrets from Compromised Browsers
Eskandarian, S., Sethi, T. K., Subbiah, V., Michael Backes, A., Pellegrino, G., Boneh, D., Cogan, J., Birnbaum, S., Brandon, P. C. W., Franke, D., et al. (2019) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2019.00036 · Cited by 47
Private Aggregation from Fewer Anonymous Messages
Ghazi, B., Manurangsi, P., Pagh, R., & Velingker, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45724-2_27 · Cited by 50
Privacy and utility preserving sensor-data transformations
Malekzadeh, M., Clegg, R. G., Cavallaro, A., & Haddadi, H. (2020) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2020.101132 · Cited by 44
Blinder -- Scalable, Robust Anonymous Committed Broadcast
Abraham, I., Benny Pinkas, B., & Yanai, A. (2020) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3372297.3417261 · Cited by 43
Roth, E., Noble, D., Hemenway, B., & Haeberlen, A. (2019) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3341301.3359660 · Cited by 43
Dopamine: Differentially Private Federated Learning on Medical DataPreprint
Malekzadeh, M., Hasırcıoglu, B., Mital, N., Katarya, K., Özfatura, E., & Gündüz, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.11693 · Cited by 66
“DP mechanisms are mostly used for computing aggregated statistics, such as the sum, mode, mean, most frequent, or histogram of the dataset (Bittau et al. 2017; Ding, Kulka-rni, and Yekhanin 2017; Cormode et al. 2018).”
MethodAre We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems
Jin, J., McMurtry, E., Rubinstein, B. I. P., & Ohrimenko, O. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833672 · Cited by 46
Secure Multi-party Computation of Differentially Private Heavy Hitters
Böhler, J., & Kerschbaum, F. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484557 · Cited by 41
Federated learning and privacy
Bonawitz, K., Kairouz, P., McMahan, H. B., & Ramage, D. (2022) · Communications of the ACM · doi:10.1145/3500240 · Cited by 40
A Survey and Guideline on Privacy Enhancing Technologies for Collaborative Machine Learning
Soykan, E. Ü., Karaçay, L., Karakoç, F., & Tomur, E. (2022) · IEEE Access · doi:10.1109/access.2022.3204037 · Cited by 45
Computing Local Sensitivities of Counting Queries with Joins
Tao, Y., He, X., Machanavajjhala, A., & Roy, S. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3389762 · Cited by 39
Wang, Y., Ding, Z., Kifer, D., & Zhang, D. (2020) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3372297.3417282 · Cited by 39
“1 INTRODUCTION Differential privacy [27] has been adopted in major data sharing initiatives by organizations such as Google [15, 29], Apple [48], Microsoft [22], Uber [36] and the U.”
BackgroundMethodOn the Rényi Differential Privacy of the Shuffle Model
Girgis, A. M., Data, D., Diggavi, S. N., Suresh, A. T., & Kairouz, P. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484794 · Cited by 46
“Such a shuffling can be enabled through anonymization techniques [10, 20, 22].”
BackgroundUnderstanding Unintended Memorization in Language Models Under Federated Learning
Thakkar, O. D., Ramaswamy, S., Mathews, R., & Beaufays, F. (2021) · Proceedings of the Third Workshop on Privacy in Natural Language Processing · doi:10.18653/v1/2021.privatenlp-1.1 · Cited by 39
On Large-Cohort Training for Federated LearningPreprint
Charles, Z., Garrett, Z., Huo, Z., Shmulyian, S., & Smith, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07820 · Cited by 50
“For example, when using the distributed differential privacy model [6, 11, 16, 65] in federated learning, noise is typically added to the updates sent from the clients to the server [54].”
MethodA Multi-Shuffler Framework to Establish Mutual Confidence for Secure Federated Learning
Zhou, Z., Xu, C., Wang, M., Kuang, X., Zhuang, Y., & Yu, S. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3215574 · Cited by 40
“The primary shuffle model Encode-Shuffle-Analyze (ESA) stems from PROCHLO [35].”
BackgroundMethodChowdhury, A. R., Wang, C., He, X., Machanavajjhala, A., & Jha, S. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3380596 · Cited by 42
“The first approach implements the CSP inside a trusted execution environment (TEE) [10, 20, 83].”
BackgroundMethodImproving utility and security of the shuffler-based differential privacy
Wang, T., Ding, B., Xu, M., Huang, Z., Hong, C., Zhou, J., Li, N., & Jha, S. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3424573.3424576 · Cited by 38
“The shuffling idea was originally proposed in Prochlo [15].”
InfluentialWagh, S., He, X., Machanavajjhala, A., & Mittal, P. (2021) · Communications of the ACM · doi:10.1145/3418290 · Cited by 38
“We will use one of these systems called Prochlo [8, 16] to illustrate the key idea of how anonymity can help improve the accuracy of such applications.”
BackgroundMethodInfluentialDistributed 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
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and VectorizationPreprint
Subramani, P., Vadivelu, N., & Kamath, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09063 · Cited by 40
Sun, K., Xu, H., Hua, K., Lin, X., Li, G., Jiang, T., & Li, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3370991 · Cited by 33
Ghazi, B., Golowich, N., Kumar, R., Pagh, R., & Velingker, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77883-5_16 · Cited by 33
“veral recent works that study differentially private algorithms in the central or local models for various statistical and learning tasks [25, 43, 47, 8, 14, 13, 15]. More recently, the shuffled model [10, 30, 16] has emerged as a middle ground between central and local differential privacy. A number of works have soug…”
InfluentialBoenisch, F., Dziedzic, A., Schuster, R., Shamsabadi, A. S., Shumailov, I., & Papernot, N. (2023) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp57164.2023.00023 · Cited by 38
“Through combination with SA [2], [12], [29] or similar aggregation methods [7], where the server can only view aggregated updates, DDP ostensibly ensures that the server cannot extract individual data points.”
BackgroundSenate: A Maliciously-Secure MPC Platform for Collaborative AnalyticsPreprint
Poddar, R., Kalra, S., Yanai, A., Deng, R., Popa, R. A., & Hellerstein, J. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13752 · Cited by 31
“wever, all these systems assume the adversaries are semi-honest and optimize for this use case, while Senate provides security against malicious adversaries. Prio [21], Melis et al. [59], and Prochlo [11] 16 collect aggregate statistics across many users, as opposed to general-purpose SQL. Further, the first two target…”
BackgroundLocally Private Gaussian EstimationPreprint
Joseph, M., Kulkarni, J., Mao, J., & Wu, Z. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08382 · Cited by 29
“Since its introduction [11], a rich line of work has made differential privacy a compelling privacy guarantee (see Dwork et al. [12] and Vadhan [24] for surveys), and deployments of differential privacy now exist at many organizations, including Apple [2], Google [5, 13], Microsoft [8], Mozilla [3], and the US Census…”
BackgroundScenario-based Adaptations of Differential Privacy: A Technical Survey
Zhao, Y., Du, J. T., & Chen, J. (2024) · ACM Computing Surveys · doi:10.1145/3651153 · Cited by 28
Liu, Z., & Tromer, E. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15802-5_26 · Cited by 28
Confidentiality Protection in the 2020 US Census of Population and Housing
Abowd, J. M., & Hawes, M. B. (2022) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-010422-034226 · Cited by 28
Distributed, Private, Sparse Histograms in the Two-Server Model
Bell, J., Gascón, A., Ghazi, B., Kumar, R., Manurangsi, P., Raykova, M., & Schoppmann, P. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3559383 · Cited by 27
“Recall that the shuffle model of DP [11, 23, 33] is an intermediate model between the local and central models of DP, where the client sends messages to a trusted shuffler that randomly permutes the messages of all users together before sending them to the analyzer.”
BackgroundOblivious coopetitive analytics using hardware enclaves
Dave, A., Leung, C., Popa, R. A., Gonzalez, J. E., & Stoica, I. G. (2020) · European Conference on Computer Systems · doi:10.1145/3342195.3387552 · Cited by 29
Decision Tree-Based Federated Learning: A Survey
Wang, Z., & Gai, K. (2024) · Blockchains · doi:10.3390/blockchains2010003 · Cited by 28
User-Defined Privacy-Preserving Traffic Monitoring Against n-by-1 Jamming Attack
Li, M., Zhu, L., Zhang, Z., Lal, C., Conti, M., & Alazab, M. (2022) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3157654 · Cited by 26
On the Power of Multiple Anonymous Messages
Ghazi, B., Golowich, N., Kumar, R., Pagh, R., & Velingker, A. (2019) · arXiv · doi:10.48550/arxiv.1908.11358 · Cited by 26
“Bittau et al. [10] introduced the Encode-Shuffle-Analyze (ESA) framework as a “middle ground” between central and local differential privacy.”
BackgroundMethodIntegrating Technical and Legal Concepts of Privacy
Sokolovska, A., & Kocarev, L. (2018) · IEEE Access · doi:10.1109/access.2018.2836184 · Cited by 24
“This property enables the construction of differentially private programming platforms [54], [55], [64]–[66].”
BackgroundThe power of factorization mechanisms in local and central differential privacy
Edmonds, A., Nikolov, A., & Ullman, J. (2020) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3357713.3384297 · Cited by 25
“Proof of Lemma 17.”
BackgroundNovikova, E., Fomichov, D., Kholod, I., & Filippov, E. (2022) · Sensors · doi:10.3390/s22082983 · Cited by 24
“Most of them [31–33] are based on the encode, shuffle, and analyse (ESA) model proposed by Bitau et al. [34] that guarantees the privacy through anonymization.”
BackgroundDifferentially Private Histograms in the Shuffle Model from Fake Users
Cheu, A., & Zhilyaev, M. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833614 · Cited by 24
Securely Sampling Biased Coins with Applications to Differential Privacy
Champion, J., Shelat, A. A., & Ullman, J. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3354256 · Cited by 24
“[16] for a textbook treatment) and has now been implemented by companies such as Apple [37, 38] and Google [5, 18] and statistical agencies such as the U.”
MethodInfluentialReal-time and private spatio-temporal data aggregation with local differential privacy
Xiong, X., Liu, S., Li, D., Cai, Z., & Niu, X. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102633 · Cited by 24
Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches
Cecaj, A., Lippi, M., Mamei, M., & Zambonelli, F. (2021) · IoT · doi:10.3390/iot2010003 · Cited by 27
“Other works instead deal with the problem of privacy but without characterizing the crowd such as, for example [118].”
BackgroundSun, X., Wang, Y., Duan, P., Zia, Q., Wang, W., & Cai, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3358799 · Cited by 22
Roth, E., Newatia, K., Ma, Y., Zhong, K., Angel, S., & Haeberlen, A. (2021) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3477132.3483585 · Cited by 24
Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential Privacy
Ma, C., Yuan, L., Han, L., Ding, M., Bhaskar, R., & Li, J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3137047 · Cited by 22
LDPRecover: Recovering Frequencies from Poisoning Attacks Against Local Differential Privacy
Sun, X., Ye, Q., Hu, H., Duan, J., Wo, T., Xu, J., & Yang, R. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00132 · Cited by 22
Bagdasaryan, E., Kairouz, P., Mellem, S., Gascón, A., Bonawitz, K., Estrin, D. L., & Gruteser, M. (2022) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2022-0104 · Cited by 38
“If approximate differential privacy, rather than pure differential privacy, is acceptable, shuffling techniques also scale better in terms of the number of participants but similarly require a trusted party or a set of outside trusted parties to conduct the shuffling task [11].”
BackgroundA comprehensive survey and taxonomy on privacy-preserving deep learning
Tran, A.-T., Luong, T.-D., & Huynh, V.-N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127345 · Cited by 21
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates
Li, Z., Liu, L., Zhang, J., & Liu, J. (2021) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads53394.2021.00052 · Cited by 25
“Different from existing methods, our method does not rely on the prior knowledge of the client’s data distribution or clients’ identities, and therefore can be applied along with existing techniques such as secure shuffling [20] and differential privacy [21] to ensure user’s”
MethodShuffle Differential Private Data Aggregation for Random Population
Wang, S., Luo, X., Qian, Y., Zhu, Y., Chen, K., Chen, Q., Xin, B., & Yang, W. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3247541 · Cited by 20
Reaching Data Confidentiality and Model Accountability on the CalTrain
Gu, Z., Jamjoom, H. T., Su, D., Huang, H., Zhang, J., Ma, T., Pendarakis, D. E., & Molloy, I. (2019) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2019.00044 · Cited by 21
“SGX was also adopted for sensitive data analytics, processing, and search, e.g., VC3 [49], Opaque [50], SecureKeeper [51], PROCHLO [52], SafeBricks [53], Oblix [54], and HardIDX [55].”
BackgroundDifferentially Private Set Union
Gopi, S., Gulhane, P., Kulkarni, J., Shen, J. H., Shokouhi, M., & Yekhanin, S. (2021) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.780 · Cited by 19
Metadata
- Authors
- Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnes, J., & Seefeld, B.
- Affiliations
- Google (United States) (US)
- University of Toronto (CA)
- Venue
- Proceedings of the 26th Symposium on Operating Systems Principles
- Publisher
- ACM
- Published
- 2017-10-14
- Pages
- 441-459
- OpenAlex
- W2761138375
- Cited by
- 378
- References
- 50
- Citations by year
- 2026: 22 · 2025: 37 · 2024: 55 · 2023: 46 · 2022: 50 · 2021: 70 · 2020: 46 · 2019: 36 · 2018: 12 · 2017: 4
- FWCI
- 11.96
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGOPS Symposium on Operating Systems Principles (SOSP) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- TSpace (University of Toronto) (submittedVersion)
- arXiv (Cornell University)
- 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
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- user privacy, privacy-preserving monitoring, telemetry, error reporting, demographic profiling, system architecture
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