The DOI matches its Crossref record; 24 of the 28 references it cites were found in a registry or an index.
The Privacy Blanket of the Shuffle Model
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“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]
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“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]
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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
Research works printed but not linked: 2
- Balle, B., Bell, J., Gascón, A., Nissim, K.: The privacy blanket of the shuffle model. CoRR abs/1903.02837 (2019). http://arxiv.org/abs/1903.02837 · arXiv
- 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
Items 201–214 of 214
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
Differential Privacy for Decentralized Learning
Cyffers, E. (2024) · theses.fr (ABES) · doi:10.70675/a3e85b6ezd607z4616zb7f1zdd23c24f937c
SilentNoise: Non-Interactive Noise Generation for Differential Privacy With Malicious Security
Eriguchi, R., Murakami, T., Ohara, K., & Attrapadung, N. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3694826
Liu, D., Fan, G. F., Guo, Q., Zhang, R., & Pan, L. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2026.3693692
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
Bouabba, S., Zeitouni, K., Haidar, B., Agoulmine, N., & Dagdia, Z. C. (2026) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm71479.2026.00041
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
Linear-Time Correlation-Controlled Shuffling on Random Regular Graphs with Spectral Guarantees
Gasimov, V. A., Mammadzada, N. F., Mustafayeva, E., Mammadov, J. I., & Aliyeva, K. R. (2026) · Information · doi:10.3390/info17090830
Privacy-preserving methodologies against privacy attacks on deep learning: a survey
Ghosh, S., & Mandal, A. K. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-026-02865-4
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
PA-CFL: Privacy-Preserving Adaptive Clustered Federated Learning Under Heterogeneous Data
Zhang, T., Wenting, S., Zhang, H., & Zhao, C. (2026) · Mathematics · doi:10.3390/math14203673
Zhang, X., Chen, H., & Li, C. (2026) · 電腦學刊 · doi:10.63367/199115992026103705007
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs
Girgis, A. M., Data, D., Diggavi, S., Kairouz, P., & Suresh, A. T. (2020) · arXiv · doi:10.48550/arxiv.2008.07180 · Cited by 16
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
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