The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Minimax Optimal Procedures for Locally Private Estimation
Open accessAbstract
Working under a model of privacy in which data remain private even from the statistician, we study the tradeoff between privacy guarantees and the risk of the resulting statistical estimators. We develop private versions of classical information-theoretical bounds, in particular those due to Le Cam, Fano, and Assouad. These inequalities allow for a precise characterization of statistical rates under local privacy constraints and the development of provably (minimax) optimal estimation procedures. We provide a treatment of several canonical families of problems: mean estimation and median estimation, generalized linear models, and nonparametric density estimation. For all of these families, we provide lower and upper bounds that match up to constant factors, and exhibit new (optimal) privacy-preserving mechanisms and computationally efficient estimators that achieve the bounds. Additionally, we present a variety of experimental results for estimation problems involving sensitive data, including salaries, censored blog posts and articles, and drug abuse; these experiments demonstrate the importance of deriving optimal procedures. Supplementary materials for this article are available online.
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Cites
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MethodRobust Stochastic Approximation Approach to Stochastic Programming
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A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers
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Cited by
Local Differential Privacy-Based Federated Learning for Internet of Things
Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., & Lam, K.-Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3037194 · Cited by 456
Dong, J., Roth, A., & Su, W. J. (2019) · arXiv · doi:10.48550/arxiv.1905.02383 · Cited by 389
Optimal schemes for discrete distribution estimation under locally differential privacy
Ye, M., & Barg, A. (2018) · IEEE Transactions on Information Theory · doi:10.1109/tit.2018.2809790 · Cited by 158
Clustered federated learning with adaptive local differential privacy on heterogeneous IoT data
He, Z., Wang, L., & Cai, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3299947 · Cited by 143
Preserving Privacy in the Internet of Connected Vehicles
Ghane, S., Jolfaei, A., Kulik, L., Ramamohanarao, K., & Puthal, D. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2964410 · Cited by 105
Metadata
- Authors
- Duchi, J. C., Jordan, M. I., & Wainwright, M. J.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2018-01-02
- Volume
- 113
- Issue
- 521
- Pages
- 182-201
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2342135826
- Cited by
- 386
- References
- 40
- FWCI
- 16.73
- Type
- journal article
- Language
- en
- Open access
- green
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