Differentially Private Event Sequences over Infinite Streams with Relaxed Privacy Guarantee

Xuebin Ren*, Shuyang Wang, Xianghua Yao, Chia Mu Yu, Wei Yu, Xinyu Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Continuous publication of statistics over user-generated streams can provide timely data monitoring and analysis for various applications. Nonetheless, such published statistics may reveal the details of individuals’ sensitive status or activities. To guarantee the privacy for event occurrences in data streams, based on the known privacy standard of $$\varepsilon $$ -differential privacy, w-event privacy has been proposed to hide multiple events occurring at continuous time instances. Nonetheless, the too strict requirement of w-event privacy makes it hard to achieve effective privacy protection with high data utility in many real-world scenarios. To this end, in this paper we propose a novel notion of average w-event privacy and the first Lyapunov optimization-based privacy-preserving scheme on infinite streams, aiming to obtain higher data utility while satisfying a relatively stable privacy guarantee for whole streams. In particular, we first formulate both our proposed privacy definition and the utility loss function of statistics publishing in a stream setting. We then design a Lyapunov optimization-based scheme with a detailed algorithm to maximize the publishing data utility under the requirement of our privacy notion. Finally, we conduct extensive experiments on both synthetic and real-world datasets to confirm the effectiveness of our scheme.

Original languageEnglish
Title of host publicationWireless Algorithms, Systems, and Applications - 14th International Conference, WASA 2019, Proceedings
EditorsEdoardo S. Biagioni, Yao Zheng, Siyao Cheng
PublisherSpringer Verlag
Pages272-284
Number of pages13
ISBN (Print)9783030235963
DOIs
StatePublished - 2019
Event14th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2019 - Honolulu, United States
Duration: 24 Jun 201926 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11604 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2019
CountryUnited States
CityHonolulu
Period24/06/1926/06/19

Keywords

  • Data release
  • Differential privacy
  • Infinite data stream

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