The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
The Privacy Blanket of the Shuffle Model
Open accessOverview
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
“Bounded gradient variance, which is a widely used assumption to analyze stochastic gradient methods, is often used when clients use stochastic local updates [305, 303, 304, 469, 265].”
From Advances and Open Problems in Federated Learning (2021) · cites this paper as [304]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“This algorithm is extended by Li et al. [304] to the non-IID case.”
From Advances and Open Problems in Federated Learning (2021) · cites this paper as [304]
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
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
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865
Probability theory the logic of science
Jaynes, E. T., & Bretthorst, G. L. (2003) · Cited by 4,600
Probability Theory: The Logic of Science
Jaynes, E. T. (2003) · doi:10.1017/cbo9780511790423 · Cited by 4,045
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Erlingsson, Ú., Pihur, V., & Korolova, A. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660348 · Cited by 1,838
Concentration Inequalities: A Nonasymptotic Theory of Independence
Boucheron, S., Lugosi, G., & Massart, P. (2013) · doi:10.1093/acprof:oso/9780199535255.001.0001 · Cited by 1,768
Lectures on the Coupling Method
Lindvall, T. (1992) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,160
Collecting Telemetry Data PrivatelyPreprint
Ding, B., Kulkarni, J., & Yekhanin, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.01524 · Cited by 519
Protection Against Reconstruction and Its Applications in Private Federated LearningPreprint
Bhowmick, A., Duchi, J. C., Freudiger, J., Kapoor, G., & Rogers, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00984 · Cited by 420
Prochlo: Strong Privacy for Analytics in the Crowd
Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnes, J., & Seefeld, B. (2017) · Proceedings of the 26th Symposium on Operating Systems Principles · doi:10.1145/3132747.3132769 · Cited by 378
Kasiviswanathan, S. P., Lee, H. K., Kobbi Nissim, B., Raskhodnikova, S., & Smith, A. (2008) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2008.27 · Cited by 373
Amplification 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
Discrete Distribution Estimation under Local PrivacyPreprint
Kairouz, P., Bonawitz, K., & Ramage, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.07387 · Cited by 268
Distributed 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
Balle, B., & Wang, Y.-X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.06530 · Cited by 252
Self-citationDistributed Private Data Analysis: Simultaneously Solving How and What
Beimel, A., Kobbi Nissim, B., & Omri, E. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85174-5_25 · Cited by 164
Self-citationPrivacy Amplification by Subsampling: Tight Analyses via Couplings and DivergencesPreprint
Balle, B., Barthe, G., & Gaboardi, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.01647 · Cited by 154
Self-citationIshai, Y., Eyal Kushilevitz, Z., Ostrovsky, R., & Sahai, A. (2006) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2006.25 · Cited by 142
Privacy Amplification by Iteration
Feldman, V., Mironov, I., Talwar, K., & Thakurta, A. (2018) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2018.00056 · Cited by 137
Probabilistic relational reasoning for differential privacy
Barthe, G., Köpf, B., Olmedo, F., & Zanella-Béguelin, S. (2012) · ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL) · doi:10.1145/2103656.2103670 · Cited by 131
Proving Differential Privacy via Probabilistic Couplings
Barthe, G., Gaboardi, M., Grégoire, B., Hsu, J., & Strub, P.-Y. (2016) · ACM/IEEE Symposium on Logic in Computer Science (LICS) · doi:10.1145/2933575.2934554 · Cited by 106
Probabilistic relational reasoning for differential privacy
Barthe, G., Köpf, B., Olmedo, F., & Zanella-Béguelin, S. (2012) · ACM SIGPLAN Notices · doi:10.1145/2103621.2103670 · Cited by 102
Probabilistic Relational Reasoning for Differential Privacy
Barthe, G., Köpf, B., Olmedo, F., & Zanella-Béguelin, S. (2013) · ACM Transactions on Programming Languages and Systems · doi:10.1145/2492061 · Cited by 100
Optimal Lower Bound for Differentially Private Multi-party Aggregation
Chan, T.-H. H., Shi, E., & Song, D. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33090-2_25 · Cited by 82
Barthe, G., & Olmedo, F. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39212-2_8 · Cited by 67
Advanced Probabilistic Couplings for Differential Privacy
Barthe, G., Fong, N., Gaboardi, M., Grégoire, B., Hsu, J., & Strub, P.-Y. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2978391 · Cited by 63
The 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
The Role of Interactivity in Local Differential Privacy
Joseph, M., Mao, J., Neel, S., & Roth, A. (2019) · arXiv · doi:10.48550/arxiv.1904.03564
The Privacy Blanket of the Shuffle Model
Balle, B., Bell, J., Gascon, A., & Nissim, K. (2019) · arXiv · doi:10.48550/arxiv.1903.02837
Self-citation
Research works printed but not linked: 1
- Kairouz, P., Oh, S., Viswanath, P.: Extremal mechanisms for local differential privacy. J. Mach. Learn. Res. 17, 17:1–17:51 (2016)
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
Self-citationDistributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey
Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3218527 · Cited by 439
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
Local differential privacy and its applications: A comprehensive survey
Yang, M., Guo, T., Zhu, T., Tjuawinata, I., Zhao, J., & Lam, K. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103827 · Cited by 308
A Field Guide to Federated OptimizationPreprint
Wang, J., Charles, Z., Xu, Z., Joshi, G., McMahan, H. B., y Arcas, B. A., Al-Shedivat, M., Andrew, G., Avestimehr, S., Daly, K., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.06917 · Cited by 467
Metadata
- Authors
- Balle, B., Bell, J., Gascón, A., & Nissim, K.
- Affiliations
- Georgetown University (US)
- The Alan Turing Institute (GB)
- University of Warwick (GB)
- Corresponding author
- Bell, James
- Gascón, Adrià
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 638-667
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2921572731
- Cited by
- 214
- References
- 28
- Citations by year
- 2026: 19 · 2025: 22 · 2024: 33 · 2023: 34 · 2022: 35 · 2021: 36 · 2020: 24 · 2019: 10
- FWCI
- 10.04
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1565387)
- Alan Turing Institute (EP/N510129/1., N510129)
- Government of the United Kingdom
- University of Cambridge
- University of Warwick (EP/N510129/1)
- Engineering and Physical Sciences Research Council (N510129, EP/N510129/, EP/N510129/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- shuffle model, differential privacy, privacy amplification, local differential privacy, lower bounds, privacy-utility trade-off
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
