Local Differential Privacy Protocol for Making Key–Value Data Robust Against Poisoning Attacks

Hikaru Horigome, Hiroaki Kikuchi*, Chia Mu Yu

*此作品的通信作者

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

Local differential privacy is a technique for concealing a user’s information from collectors by randomizing the information within the user’s own device before sending it to unreliable collectors. Ye et al. proposed PrivKV, a local differential privacy protocol for securely collecting key–value data, which comprises two-dimensional data with discrete and continuous values. However, such data is vulnerable to a “poisoning attack,” whereby a fake user sends data to manipulate the key-value dataset. To address this issue, we propose an Expectation-Maximization (EM) based algorithm, in conjunction with a cryptographical protocol for ensuring secure random sampling. Our local differential privacy protocol, called emPrivKV, offers two main advantages. First, it is able to estimate statistical information more accurately from randomized data. Second, it is robust against manipulation attacks such as poisoning attacks, whereby malicious users manipulate a set of analysis results by sending altered information to the aggregator without being detected. In this paper, we report on the improvement in the accuracy of statistical value estimation and the strength of the robustness against poisoning attacks achieved by applying the proposed method to open datasets.

原文English
主出版物標題Modeling Decisions for Artificial Intelligence - 20th International Conference, MDAI 2023, Proceedings
編輯Vicenç Torra, Yasuo Narukawa
發行者Springer Science and Business Media Deutschland GmbH
頁面241-252
頁數12
ISBN(列印)9783031334979
DOIs
出版狀態Published - 2023
事件20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023 - Umeå, 瑞典
持續時間: 19 6月 202322 6月 2023

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13890 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023
國家/地區瑞典
城市Umeå
期間19/06/2322/06/23

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