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Efficient and accurate strategies for differentially-private sliding window queries

Published: 18 March 2013 Publication History

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 streaming 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 simultaneously, 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 answered 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.

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cover image ACM Other conferences
EDBT '13: Proceedings of the 16th International Conference on Extending Database Technology
March 2013
793 pages
ISBN:9781450315975
DOI:10.1145/2452376
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 18 March 2013

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  • (2023)Variance Value Stream Data Publishing Based on Differential Privacy Under Wearable DevicesProceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition10.1145/3641584.3641806(1470-1478)Online publication date: 22-Sep-2023
  • (2023)Differential Privacy Frequent Closed Itemset Mining over Data Stream2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom60117.2023.00124(865-872)Online publication date: 1-Nov-2023
  • (2022)Delay-tolerant Privacy-preserving Continuous Histogram Publishing MethodProceedings of the 7th International Conference on Big Data and Computing10.1145/3545801.3545814(88-95)Online publication date: 27-May-2022
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  • (2022)Defeating traffic analysis via differential privacy: a case study on streaming trafficInternational Journal of Information Security10.1007/s10207-021-00574-321:3(689-706)Online publication date: 30-Jan-2022
  • (2021)Face Image Publication Based on Differential PrivacyWireless Communications and Mobile Computing10.1155/2021/66807012021(1-20)Online publication date: 6-Jan-2021
  • (2021)Privacy-Preserving Continuous Data Collection for Predictive Maintenance in Vehicular Fog-CloudIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.301193122:8(5060-5070)Online publication date: Aug-2021
  • (2020)PAARS: Privacy Aware Access Regulation System2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON51285.2020.9298048(0155-0161)Online publication date: 28-Oct-2020
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