Continual Mean Estimation Under User-Level Privacy

George, AJ and Ramesh, L and Singh, AV and Tyagi, H (2024) Continual Mean Estimation Under User-Level Privacy. In: IEEE Journal on Selected Areas in Information Theory, 5 . pp. 28-43.

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Abstract

We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary order. Until now these requirements of continual release and user-level privacy were considered in isolation. But, in practice, both these requirements come together as the users often contribute data repeatedly and multiple queries are made. We provide an algorithm that outputs a mean estimate at every time instant t such that the overall release is user-level � -DP and has the following error guarantee: Denoting by mt the maximum number of samples contributed by a user, as long as �Ω (1/�) users have mt/2 samples each, the error at time t is �(Formula presented). This is a universal error guarantee which is valid for all arrival patterns of the users. Furthermore, it (almost) matches the existing lower bounds for the single-release setting at all time instants when users have contributed equal number of samples. © 2020 IEEE.

Item Type:	Journal Article
Publication:	IEEE Journal on Selected Areas in Information Theory
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Information theory, Arbitrary order; Arrival patterns; Differential privacies; Low bound; Multiple queries; Noise measurements; Number of samples; Privacy; Sensitivity; User levels, Errors
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	29 Aug 2024 12:56
Last Modified:	29 Aug 2024 12:56
URI:	http://eprints.iisc.ac.in/id/eprint/84898

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