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.
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“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]
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Cites
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Items 301–368 of 368
DuetSGX: Differential Privacy with Secure HardwarePreprint
Nguyen, P., Silence, A., Darais, D., & Near, J. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10664
“Ongoing research in the local model of differential privacy has produced a significant number of systems, including RAPPOR [16], Prochlo [9], Apple’s system [1], and many other works in various kinds of analytics [23, 31] and machine learning [13].”
BackgroundFairly Private Through Group Tagging and Relation Impact
Sengupta, P., & Mishra, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85529-1_21
“PROCHLO [10] 2017 Encode Shuffle and Analyse (ESA) for strong CDP Adding DP to Iterative Training Procedures [11] 2018 Introduces an algorithm with the modular approach to minimize the changes on training algorithm, provides a variety of configuration strategies for the privacy mechanism.”
Privacy Enhancement via Dummy Points in the Shuffle ModelPreprint
Li, X., Liu, W., Feng, H., Huang, K., Liu, J., Ren, K., & Qin, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.13738
Integrity and privacy in adversarial machine learning
Jagielski, M. (2021) · Digital Repository Service (Northeastern University) · doi:10.17760/d20413920
Infinitely Divisible Noise in the Low Privacy Regime
Pagh, R., & Stausholm, N. M. (2021) · Research at the University of Copenhagen (University of Copenhagen) · doi:10.48550/arxiv.2110.06559
Communicating Differential Privacy Models by Illustrations: A Survey and In-Depth Interview Study
Wu, C., Wang, T., Proctor, R. W., Li, N., Blocki, J., & Xiong, A. (2021) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181321651137
RECENT PROGRESS OF DIFFERENTIALLY PRIVATE FEDERATED LEARNING WITH THE SHUFFLE MODEL
Ahmed, M. A. M., Wu, S., Dushime, L. D., & Tao, Y.-H. (2021) · International Journal of Engineering Technologies and Management Research · doi:10.29121/ijetmr.v8.i11.2021.1028
“The framework of DP-FL with shuffle model consists of users, shuffle and analyzer [Balle et al. (2019), Meehan et al. (2021), Erlingsson et al. (2019), Bittau et al. (2017)].”
BackgroundMethodInfluentialSecure Computation of Differentially Private Mechanisms
Böhler, J. (2021) · Springer eBooks · doi:10.1007/978-3-642-27739-9_1714-1
Secure Computation of Differentially Private Mechanisms
Böhler, J. (2021) · Springer eBooks · doi:10.1007/978-3-642-27739-9_1714-2
Pejó, B., & Desfontaines, D. (2022) · SpringerBriefs in computer science · doi:10.1007/978-3-030-96398-9_11
Roth, A. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1442610
“There is work aimed at making this general family of approaches practical in specific settings; for example, Narayan and Haeberlen (2012), Papadimitriou, Narayan, and Haeberlen (2017), and Bittau et al. (2017).”
BackgroundThe Privacy Elasticity of Behavior: Conceptualization and ApplicationPreprint
Dekel, I., Cummings, R., Heffetz, O., & Ligett, K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4145498
Efficiency-optimized Data Perturbation in Local Differentially Private Federated LearningPreprint
Zhao, J., Yang, M., Zheng, J., Feng, J., & Matwin, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1890894/v1
Tight differential privacy blanket for shuff model
Biswas, S., Jung, K., & Catuscia Palamidessi, A. (2022) · IET conference proceedings. · doi:10.1049/icp.2022.2041
“An intermediate paradigm between the central and the local models of differential privacy (DP), known as the shuffle model [2], has recently gained popularity.”
Proving Obliviousness of Probabilistic Algorithms with Formal Verification
Yan, P. (2022) · ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH) · doi:10.1145/3563768.3565547
How Good Are Privacy Guarantees? Platform Architecture and Violation of User PrivacyPreprint
Acemoğlu, D., Fallah, A., Makhdoumi, A., Malekian, A., & Ozdaglar, A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4333457
Anonymous Communication and Shuffle Model in Federated Learning
Yu, S., & Cui, L. (2022) · Digital Privacy and Security · doi:10.1007/978-981-19-8692-5_8
How Good Are Privacy Guarantees? Platform Architecture and Violation of User PrivacyPreprint
Acemoğlu, D., Fallah, A., Makhdoumi, A., Malekian, A., & Ozdaglar, A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4497994
“In closing, we should highlight that various forms of shuffling have been studied in the differential privacy literature as a technique to boost the provided privacy guarantees (see, e.g., Bittau et al. [2017] and Cheu [2021]).”
MethodOhrimenko, O. (2023) · Springer eBooks · doi:10.1007/978-3-642-27739-9_1791-1
Ravikumar, A., & Sriraman, H. (2023) · doi:10.1002/9781394213726.ch9
Amplification by Shuffling without Shuffling
Balle, B., Bell, J., & Gascón, A. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623215
“[13] on the Encode, Shuffle and Analyze framework proposed to instantiate a trusted shuffler using a trusted execution environment (e.”
BackgroundYang, Q., Du, X., Wu, X., Wang, N., Wang, W., & Liu, A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4646455
Exploring Use of Explanative Illustrations to Communicate Differential Privacy Models
Xiong, A., Wu, C.-H., Wang, T., Proctor, R. W., Blocki, J., Li, N.-H., & Jha, S. (2023) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/21695067231195006 · Cited by 6
“More recently, Shuffler DP has been introduced in academia (Cheu et al., 2019) and industry (Bittau et al., 2017), which achieves a middle ground between Central DP and Local DP. Shuffler DP involves an auxiliary party called the shuffler.”
BackgroundPersonalized Differential Privacy in the Shuffle Model
Yang, R., Yang, H. Y., Fan, J., Dong, C., Pang, Y., Wong, D. S., & Wang, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-99-9785-5_33
Cookie Monster: Efficient On-Device Budgeting for Differentially-Private Ad-Measurement Systems
Tholoniat, P., Kostopoulou, K., McNeely, P., Sodhi, P. S., Varanasi, A., Case, B., Cidon, A., Geambasu, R., & Lécuyer, M. (2024) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3694715.3695965
Handling Dropouts in Federating Learning with Personal Data Management Systems
Mirval, J., Bouganim, L., & Popa, I. S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-662-69603-3_2
Histogram Publication with Shuffled Differential Privacy
Gao, Y., Yang, X., Wu, T. S., & Zhou, Y. (2024) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c63300.2024.00093
Almost Instance-optimal Clipping for Summation Problems in the Shuffle Model of Differential Privacy
Dong, W., Luo, Q., Fanti, G. C., Shi, E., & Yi, K. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690225
Secure Computation of Differentially Private Mechanisms
Böhler, J. (2025) · Springer eBooks · doi:10.1007/978-3-030-71522-9_1714
Enhancing the Resilience of Privacy-Preserving Machine Learning using Adversarial Techniques
Rengarajan, A., Senthilkumar, L., Raj, A. L., & S, A. U. (2024) · International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC) · doi:10.1109/icdscnc62492.2024.10939481
Secure multi-party shuffling with optimal communication
Chen, K.-C., Pang, B., Li, Y., & Wang, M. (2025) · The Computer Journal · doi:10.1093/comjnl/bxaf043
Enabling Anonymous Online Streaming Analytics at the Network Edge
Xiao, Y., Gu, Y., Zhao, Y., Lin, S., & Kuzmanovic, A. (2025) · ACM Transactions on Computer Systems · doi:10.1145/3746130
Prior-Based Label Differential Privacy via Secure Two-Party Computation
Agarwal, A., Peceny, S., Raykova, M., Schoppmann, P., & Seth, K. (2025) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3708821.3736203
Large-Scale Private Computation for Real-World Applications via Trusted Hardware and Obliviousness
Demertzis, I. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00336
Privacy-Preserving Threshold Enforcement Aggregation with Fault TolerancePreprint
Li, G., Jiang, Q. G., Zhou, L., Yang, L., Liu, L., & Ma, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5450718
Sellang, K. (2025) · Data Journal of Information Systems and Management · doi:10.61978/data.v3i1.921
“This not only affects the protection of consumer data but also stifles innovation within these markets as businesses grapple with the perceived trade off between rigorous privacy measures and operational efficiency (Bittau et al., 2017; Price & Cohen, 2019).”
Improving Utility of Private Join Size Estimation via Shuffling
Liu, X., Mao, Y., Zhang, M., & Li, M. (2025) · Mathematics · doi:10.3390/math13213468
MPC-FH: Privately Estimating Frequency Histogram for Advertising Measurement
Wang, P., Yang, H., Zeng, D., Xie, H. L., Zhao, J., & Guan, X. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3626789
PRIMA: Privacy preserving Multi-dimensional Analytic Approach
Wang, Y., Cheng, X., Zhang, P., & Wei, A. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761310
PEGS: A Graph Synthesis Approach Based on Local Differential Privacy Preference
Hou, L., Ni, W., Fu, N., Zhang, D., & Zhang, R. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3637324
Efficient Privacy-Preserving Streaming Histogram Publication via Sampling-Based Shuffled Method
Zheng, X., Jin, Y., Wang, X., & Liu, Z. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010063
Concretely Efficient Constructions for Differentially Private Set Intersection
Peng, X. H., Wang, Y., Liu, W., Peng, L., Han, F., Duan, L., Gu, Z., Sun, J., & Hong, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6203-9_2
AdaCOS: adaptive differential privacy shuffle model based on cosine similarity
Ye, Z., Wang, G., Ma, C., Guo, Z., & Huang, H. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00397-5
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
Answering Range Queries for Arbitrary Distribution Under Shuffled Differential Privacy
Wang, L., Ye, Q., Hu, H., & Meng, X. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3657305
Efficient fault-tolerant quantum protocol for differential privacy in the shuffle model
Asghar, H. J., Mukherjee, A., & Brennen, G. K. (2026) · Quantum Information Processing · doi:10.1007/s11128-026-05087-3
DISSEC-ML ˸ towards distributed and secured machine learning in the personal cloud
Mirval, J. (2024) · theses.fr (ABES) · doi:10.70675/50fbf37bzaf6bz4f4bzb292zd8a7e1a5d8f4
Enhanced Shuffle Models for Optimized Differential Privacy in Federated Learning
Zhu, H. (2026) · Chinese Journal of Electronics · doi:10.23919/cje.2025.00.120
Meng, X. (2026) · Big data management · doi:10.1007/978-981-95-5787-5_6
Liu, D., Fan, G. F., Guo, Q., Zhang, R., & Pan, L. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2026.3693692
Auditing Differentially Private Interactive Database Systems
Sharma, S., Zhang, W., Yan, Q., & Tramèr, F. (2026) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3779208.3785288
SuperDP: Differential Privacy Refutation via Supermartingales
Chatterjee, K., Goharshady, E. K., & Žikelić, Đ. (2026) · Proceedings of the ACM on Programming Languages · doi:10.1145/3808296
Shuffling Is Universal: Statistical Additive Randomized Encodings for All Functions
Bitansky, N., Erabelli, S., Garg, R. M., & Ishai, Y. (2026) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3798129.3800890
Making Privacy Public: Toward a Differential Privacy Deployment Registry
Nanayakkara, P., Ghazi, E., & Vadhan, S. (2026) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp63933.2026.00157
CHORUS: Secret Recovery with Ephemeral Client Committees
Rathee, D., Dauterman, E., Li, A., & Popa, R. A. (2026) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp63933.2026.00149
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Riya, F. F., Kotevska, O., & Sun, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-33260-8_3
Local Differential Privacy for Privacy-preserving Machine Learning
Graham, C. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1561/978-1-63828-477-220251004
Multi-Shuffler: Decentralized Shuffling for Differential Privacy in Mobile Edge Computing
Chen, W., Huang, X., Zhu, Y., Han, T., Tong, W., & Zhong, S. (2026) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/2575-8411.2026.00040
Bonawitz, K., Kairouz, P., McMahan, H. B., & Ramage, D. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1561/978-1-63828-477-220251011
Privacy‐Aware Microgrid Networks With Federated Data Analytics
Liu, S., Wu, W., Wang, S., & Li, J. (2026) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70490
Personalized Shuffled Differential Privacy for Set-Valued Data
Yang, R., & Zhong, G. (2026) · International Conference on Machine Learning, Natural Language Processing and Modeling · doi:10.1145/3832928.3832958
Evolving federated density peak clustering with shuffler-based anonymization
Ožbot, M., Ozawa, S., & Škrjanc, I. (2026) · Evolving Systems · doi:10.1007/s12530-026-09884-0
“To avoid revealing which client produced which peaks, we use an additional shuffler-based aggrega - tion layer between the clients and the server (Bittau et al. 2017).”
Architectural Models for Privacy-Preserving Data Collection in Mobile Educational Applications
Ilievska, G., Trajcheska, J., Dimitrova, V., & Dodevska, M. (2026) · International Conference on Informatics and Information Technologies (CIIT) · doi:10.1109/ciit68512.2026.11731554
Differentially-Private "Draw and Discard" Machine Learning
Pihur, V., Korolova, A., Liu, F., Sankuratripati, S., Yung, M., Huang, D., & Zeng, R. (2018) · arXiv · doi:10.48550/arxiv.1807.04369 · Cited by 25
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
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