The Privacy Blanket of the Shuffle Model
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Bounded gradient variance is often used when clients use stochastic local updates.
“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]
Section 5.3 (pp. 22–23) discusses the variance of privacy-amplification random variables, not gradient variance. Searches for bounded gradient variance, stochastic gradient methods, and stochastic local updates found no passage addressing the attributed assumption. (LLM, conf=0.76)
Experimental — accuracy not yet measuredLi et al. [304] extended PD-SGD to the non-IID case.
“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]
Section 4.2.1 (p. 13) discusses an i.i.d. assumption for a lower bound on summation protocols, not an extension of PD-SGD. Searches for PD-SGD, Li et al., and a non-IID stochastic-gradient extension found no passage addressing the attributed claim. (LLM, conf=0.76)
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Scott, M., Cormode, G., & Maple, C. R. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3147643 · Cited by 12 · see citing works
Efficient protocols for heavy hitter identification with local differential privacy
Zhao, D., Zhao, S., Chen, H., Liu, R., Li, C., & Liang, W. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0412-y · Cited by 12 · see citing works
Privacy Enhancement Via Dummy Points in the Shuffle Model
Li, X., Liu, W., Feng, H., Huang, K., Hu, Y., Liu, J., Ren, K., & Qin, Z. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3263162 · Cited by 12 · see citing works
V2V-ESP: Vehicle-to-Vehicle Energy Sharing Privacy Protection Scheme Based on SDP Algorithm
Ju, Z., & Li, Y. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3321083 · Cited by 12 · see citing works
On the Round Complexity of the Shuffle Model
Beimel, A., Haitner, I., Kobbi Nissim, B., & Stemmer, U. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64378-2_24 · Cited by 12 · see citing works
Non-Interactive Anonymous Router
Shi, E., & Wu, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77883-5_17 · Cited by 12 · see citing works
Network Shuffling: Privacy Amplification via Random Walks
Liew, S. P., Takahashi, T., Takagi, S., Kato, F., Cao, Y., & Yoshikawa, M. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3526162 · Cited by 12 · see citing works
The limits of pan privacy and shuffle privacy for learning and estimation
Cheu, A., & Ullman, J. (2021) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3406325.3450995 · Cited by 12 · see citing works
Differentially Private Numerical Vector Analyses in the Local and Shuffle Model
Wang, S., Yu, S., Ren, X., Li, J., Li, Y., Yang, W., Yan, H., & Li, J. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3340178 · Cited by 11 · see citing works
AdaSTopk: Adaptive federated shuffle model based on differential privacy
Yang, Q., Du, X., Liu, A., Wang, N., Wang, W., & Wu, X. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119186 · Cited by 11 · see citing works
Locally Private k-Means in One RoundPreprint
Chang, A., Ghazi, B., Kumar, R., & Manurangsi, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.09734 · Cited by 11 · see citing works
Differentially Private Multi-Armed Bandits in the Shuffle ModelPreprint
Tenenbaum, J., Kaplan, H. Y., Mansour, Y., & Stemmer, U. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02900 · Cited by 11 · see citing works
Differential Privacy Configurations in the Real World: A Comparative Analysis
Khavkin, M., & Toch, E. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3603731 · Cited by 11 · see citing works
Private Federated Submodel Learning with Sparsification
Vithana, S., & Ulukuş, Ş. (2022) · IEEE Information Theory Workshop (ITW) · doi:10.1109/itw54588.2022.9965815 · Cited by 10 · see citing works
Efficient Noise Generation Protocols for Differentially Private Multiparty Computation
Eriguchi, R., Ichikawa, A., Kunihiro, N., & Nuida, K. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3227568 · Cited by 10 · see citing works
Samplable Anonymous Aggregation for Private Federated Data Analysis
Talwar, K., Wang, S., McMillan, A., Feldman, V., Bansal, P., Basile, B., Cahill, Á., Chan, Y. S., Chatzidakis, M., Chen, J., et al. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690224 · Cited by 10 · see citing works
Eriguchi, R., Ichikawa, A., Kunihiro, N., & Nuida, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-662-64322-8_13 · Cited by 10 · see citing works
Frequency estimation under multiparty differential privacy
Huang, Z., Qiu, Y., Yi, K., & Cormode, G. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3547305.3547312 · Cited by 10 · see citing works
Humphries, T., Mahdavi, R. A., Veitch, S., & Kerschbaum, F. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560559 · Cited by 10 · see citing works
The power of the hybrid model for mean estimation
Avent, B., Dubey, Y., & Korolova, A. (2020) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2020-0062 · Cited by 9 · see citing works
Collecting and analyzing key-value data under shuffled differential privacy
Wang, N., Zheng, W., Wang, Z., Wei, Z., Gu, Y., Tang, P., & Yu, G. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-022-1572-0 · Cited by 9 · see citing works
Separating Local & Shuffled Differential Privacy via Histograms
Balcer, V., & Cheu, A. (2020) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itc.2020.1 · Cited by 27 · see citing works
Toward Evaluating Re-identification Risks in the Local Privacy ModelPreprint
Murakami, T., & Takahashi, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08238 · Cited by 9 · see citing works
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential PrivacyPreprint
Gu, X., Li, M., & Xiong, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.11578 · Cited by 9 · see citing works
Frequency Estimation in the Shuffle Model with Almost a Single Message
Luo, Q., Wang, Y., & Yi, K. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560608 · Cited by 8 · see citing works
- 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 · 2018: 1
- 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 › Artificial Intelligence › 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 ›
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