The Library
LDP-IDS : local differential privacy for infinite data streams
Tools
Ren, Xuebin, Shi, Liang, Yu, Weiren, Yang, Shusen, Zhao, Cong and Xu, Zongben (2022) LDP-IDS : local differential privacy for infinite data streams. In: ACM SIGMOD PODS International Conference on Management of Data, Philadelphia, PA, USA, 12-17 Jun 2022. Published in: SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data pp. 1064-1077. ISBN 9781450392495. doi:10.1145/3514221.3526190
|
PDF
WRAP-LDP-IPS-local-differential-privacy-for-infinite-data-streams-Yu-2022.pdf - Accepted Version - Requires a PDF viewer. Download (1563Kb) | Preview |
Official URL: https://doi.org/10.1145/3514221.3526190
Abstract
Local differential privacy (LDP) is promising for private streaming data collection and analysis. However, existing few LDP studies over streams either apply to finite streams only or may suffer from insufficient protection. This paper investigates this problem by proposing LDP-IDS, a novel w-event LDP paradigm to provide practical privacy guarantee for infinite streams. By constructing a unified error analysis, we adapt the existing budget division framework in centralized differential privacy (CDP) for LDP-IDS, which however incurs prohibitive noise and expensive communication cost. To this end, we propose a novel and extensible framework of population division and recycling, as well as online adaptive population division algorithms for LDP-IDS. We provide theoretical guarantees and demonstrate, through extensive discussions, that our proposed framework not only achieves significant reduction in utility loss and communication overhead, but also enjoys great compatibility for varied analytic tasks and flexibility of incorporating ideas of many existing stream algorithms. Extensive experiments on synthetic and real-world datasets validate the high effectiveness, efficiency, and flexibility of our proposed framework and methods.
Item Type: | Conference Item (Paper) | ||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||||||||||||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Data mining, Artificial intelligence, Computer security, Computer science -- Mathematics, Data encryption (Computer science) | ||||||||||||||||||||||||||||||||||||
Journal or Publication Title: | SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data | ||||||||||||||||||||||||||||||||||||
Publisher: | Association for Computing Machinery | ||||||||||||||||||||||||||||||||||||
ISBN: | 9781450392495 | ||||||||||||||||||||||||||||||||||||
Official Date: | 11 June 2022 | ||||||||||||||||||||||||||||||||||||
Dates: |
|
||||||||||||||||||||||||||||||||||||
Page Range: | pp. 1064-1077 | ||||||||||||||||||||||||||||||||||||
DOI: | 10.1145/3514221.3526190 | ||||||||||||||||||||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||||||||||||||||||||
Reuse Statement (publisher, data, author rights): | © The authors | ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2022 International Conference on Management of Data (ACM SIGMOD '22), 2022, http://dx.doi.org/10.1145/3514221.3526190. | ||||||||||||||||||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||||||||||||||||||||
Copyright Holders: | Association for Computing Machinery | ||||||||||||||||||||||||||||||||||||
Date of first compliant deposit: | 29 March 2022 | ||||||||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 13 April 2022 | ||||||||||||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
|
||||||||||||||||||||||||||||||||||||
Conference Paper Type: | Paper | ||||||||||||||||||||||||||||||||||||
Title of Event: | ACM SIGMOD PODS International Conference on Management of Data | ||||||||||||||||||||||||||||||||||||
Type of Event: | Conference | ||||||||||||||||||||||||||||||||||||
Location of Event: | Philadelphia, PA, USA | ||||||||||||||||||||||||||||||||||||
Date(s) of Event: | 12-17 Jun 2022 | ||||||||||||||||||||||||||||||||||||
Related URLs: | |||||||||||||||||||||||||||||||||||||
Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year