Private continual release of real-valued data streams

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

We present a differentially private mechanism to display statistics (e.g., the moving average) of a stream of real valued observations where the bound on each observation is either too conservative or unknown in advance. This is particularly relevant to scenarios of real-time data monitoring and reporting, e.g., energy data through smart meters. Our focus is on real-world data streams whose distribution is light-tailed, meaning that the tail approaches zero at least as fast as the exponential distribution. For such data streams, individual observations are expected to be concentrated below an unknown threshold. Estimating this threshold from the data can potentially violate privacy as it would reveal particular events tied to individuals. On the other hand an overly conservative threshold may impact accuracy by adding more noise than necessary. We construct a utility optimizing differentially private mechanism to release this threshold based on the input stream. Our main advantage over the state-of-the-art algorithms is that the resulting noise added to each observation of the stream is scaled to the threshold instead of a possibly much larger bound; resulting in considerable gain in utility when the difference is significant. Using two real-world datasets, we demonstrate that our mechanism, on average, improves the utility by a factor of 3.5 on the first dataset, and 9 on the other. While our main focus is on continual release of statistics, our mechanism for releasing the threshold can be used in various other applications where a (privacy-preserving) measure of the scale of the input distribution is required.
Original languageEnglish
Title of host publicationProceedings of the Network and Distributed System Security Symposium, NDSS 2019
PublisherInternet Society
Pages1-13
Number of pages13
ISBN (Electronic)189156255X
DOIs
Publication statusPublished - 2019
Event26th Annual Network and Distributed System Security Symposium, NDSS 2016 - San Diego, United States
Duration: 24 Feb 201927 Feb 2019

Conference

Conference26th Annual Network and Distributed System Security Symposium, NDSS 2016
CountryUnited States
CitySan Diego
Period24/02/1927/02/19

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  • Cite this

    Perrier, V., Asghar, H. J., & Kaafar, D. (2019). Private continual release of real-valued data streams. In Proceedings of the Network and Distributed System Security Symposium, NDSS 2019 (pp. 1-13). Internet Society. https://doi.org/10.14722/ndss.2019.23535