Private and Continual Release of Statistics

Author:

Chan T.-H. Hubert1,Shi Elaine2,Song Dawn3

Affiliation:

1. University of Hong Kong

2. Palo Alto Research Center

3. University of California, Berkeley

Abstract

We ask the question: how can Web sites and data aggregators continually release updated statistics, and meanwhile preserve each individual user’s privacy? Suppose we are given a stream of 0’s and 1’s. We propose a differentially private continual counter that outputs at every time step the approximate number of 1’s seen thus far. Our counter construction has error that is only poly-log in the number of time steps. We can extend the basic counter construction to allow Web sites to continually give top- k and hot items suggestions while preserving users’ privacy.

Funder

Office of Naval Research

Division of Computing and Communication Foundations

Publisher

Association for Computing Machinery (ACM)

Subject

Safety, Risk, Reliability and Quality,General Computer Science

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1. A Federated Learning Framework Based on Differentially Private Continuous Data Release;IEEE Transactions on Dependable and Secure Computing;2024-09

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