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Title: Efficient and Accurate Strategies for Differentially-Private Sliding Window Queries

Citation Type: Miscellaneous

Publication Year: 2013

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Abstract: Regularly releasing the aggregate statistics about data streams in a privacy-preserving way not only serves valuable commercial and social purposes, but also protects the privacy of individuals. This problem has already been studied under differential privacy, but only for the case of a single continuous query that covers the entire time span, e.g., counting the number of tuples seen so far in the stream. However, most real-world applications are window-based, that is, they are interested in the statistical information about stream- ing data within a window, instead of the whole unbound stream. Furthermore, a Data Stream Management System (DSMS) may need to answer numerous correlated aggregated queries simulta- neously, rather than a single one. To cope with these requirements, we study how to release differentially private answers for a set of sliding window aggregate queries. We propose two solutions, each consisting of query sampling and composition. We first selectively sample a subset of representative sliding window queries from the set of all the submitted ones. The representative queries are an- swered by adding Laplace noises in a way satisfying differential privacy. For each non-representative query, we compose its answer from the query results of those representatives. The experimental evaluation shows that our solutions are efficient and effective.

Url: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.2062&rep=rep1&type=pdf

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Authors: Cao, Jianneng; Xiao, Qian; Ghinita, Gabriel

Publisher: Purdue University

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: United States