The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Abstract
Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users’ private preferences or software usage may be monitored via such reports. We study the collection of such statistics in the local differential privacy (LDP) model, and describe an algorithm whose privacy cost is polylogarithmic in the number of changes to a user's value. More fundamentally—by building on anonymity of the users’ reports—we also demonstrate how the privacy cost of our LDP algorithm can actually be much lower when viewed in the central model of differential privacy. We show, via a new and general privacy amplification technique, that any permutation-invariant algorithm satisfying ε-local differential privacy will satisfy -central differential privacy. By this, we explain how the high noise and overhead of LDP protocols is a consequence of them being significantly more private in the central model. As a practical corollary, our results imply that several LDP-based industrial deployments may have much lower privacy cost than their advertised ε would indicate—at least if reports are anonymized.
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“Of course, these improved proof techniques often rely on additional (albeit realistic) assumptions, such as that random shuffling can be used to provide unlinkability [17] or that the intermediate model weights computed during training can be hidden from the adversary [18].”
From The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks (2018) · cites this paper as [17]
In the citing paper
“Also, without modifying the training approach, improved proof techniques have been able to improve guarantees by orders of magnitude, indicating that the analytic e is not a tight upper bound.”
From The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks (2018) · cites this paper as [17]
Citations
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Self-citationExposed! A Survey of Attacks on Private Data
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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
The Privacy Blanket of the Shuffle Model
Balle, B., Bell, J., Gascón, A., & Nissim, K. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-26951-7_22 · Cited by 214
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Ponomareva, N., Hazimeh, H., Kurakin, A., Xu, Z., Denison, C., McMahan, H. B., Vassilvitskii, S., Chien, S., & Thakurta, A. G. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14649 · Cited by 166
Self-citation
Metadata
- Authors
- Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., & Thakurta, A.
- Venue
- Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms
- Published
- 2019-01
- Pages
- 2468-2479
- Cited by
- 264
- References
- 33
- Language
- en
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