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Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory

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Kui RenAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 2, Issue 1

Article No.: 30, Pages 1 - 27

https://doi.org/10.1145/3639285

Published: 26 March 2024 Publication History

Abstract

Top-k frequent items detection is a fundamental task in data stream mining. Many promising solutions are proposed to improve memory efficiency while still maintaining high accuracy for detecting the Top-k items. Despite the memory efficiency concern, the users could suffer from privacy loss if participating in the task without proper protection, since their contributed local data streams may continually leak sensitive individual information. However, most existing works solely focus on addressing either the memory-efficiency problem or the privacy concerns but seldom jointly, which cannot achieve a satisfactory tradeoff between memory efficiency, privacy protection, and detection accuracy.

In this paper, we present a novel framework HG-LDP to achieve accurate Top-k item detection at bounded memory expense, while providing rigorous local differential privacy (LDP) protection. Specifically, we identify two key challenges naturally arising in the task, which reveal that directly applying existing LDP techniques will lead to an inferior "accuracy-privacy-memory efficiency" tradeoff. Therefore, we instantiate three advanced schemes under the framework by designing novel LDP randomization methods, which address the hurdles caused by the large size of the item domain and by the limited space of the memory. We conduct comprehensive experiments on both synthetic and real-world datasets to show that the proposed advanced schemes achieve a superior "accuracy-privacy-memory efficiency" tradeoff, saving 2300× memory over baseline methods when the item domain size is 41,270. Our code is anonymously open-sourced via the link.

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Cited By

Huang KOuyang GYe QHu HZheng BZhao XZhang RZhou X(2024)LDPGuard: Defenses Against Data Poisoning Attacks to Local Differential Privacy ProtocolsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.335890936:7(3195-3209)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3358909
Feng SMohammady MWang HLi XQin ZHong Y(2024)DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00124(1009-1027)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00124

Index Terms

Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory
1. Security and privacy
  1. Database and storage security
    1. Data anonymization and sanitization

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 2, Issue 1

SIGMOD

February 2024

1874 pages

EISSN:2836-6573

DOI:10.1145/3654807

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Published: 26 March 2024

Published in PACMMOD Volume 2, Issue 1

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Cited By

Huang KOuyang GYe QHu HZheng BZhao XZhang RZhou X(2024)LDPGuard: Defenses Against Data Poisoning Attacks to Local Differential Privacy ProtocolsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.335890936:7(3195-3209)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3358909
Feng SMohammady MWang HLi XQin ZHong Y(2024)DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00124(1009-1027)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00124

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