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
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]).”
BackgroundMethodInfluential
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