Robust Estimation Method against Poisoning Attacks for Key-Value Data with Local Differential Privacy

Hikaru Horigome*, Hiroaki Kikuchi, Masahiro Fujita, Chia Mu Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Local differential privacy (LDP) protects user information from potential threats by randomizing data on individual devices before transmission to untrusted collectors. This method enables collectors to derive user statistics by analyzing randomized data, thereby presenting a promising avenue for privacy-preserving data collection. In the context of key–value data, in which discrete and continuous values coexist, PrivKV has been introduced as an LDP protocol to ensure secure collection. However, this framework is susceptible to poisoning attacks. To address this vulnerability, we propose an expectation maximization (EM)-based algorithm combined with a cryptographic protocol to facilitate secure random sampling. Our LDP protocol, known as emPrivKV, exhibits two key advantages: it improves the accuracy of statistical information estimation from randomized data, and enhances resilience against the manipulation of statistics, that is, poisoning attacks. These attacks involve malicious users manipulating the analysis results without detection. This study presents the empirical results of applying the emPrivKV protocol to both synthetic and open datasets, highlighting a notable improvement in the precision of statistical value estimation and robustness against poisoning attacks. As a result, emPrivKV improved the frequency and the mean gains by (Formula presented.) and (Formula presented.), respectively, compared to PrivKV, with the number of fake users being (Formula presented.) of the genuine users. Our findings contribute to the ongoing discourse on refining LDP protocols for key–value data in scenarios involving privacy-sensitive information.

Original languageEnglish
Article number6368
JournalApplied Sciences (Switzerland)
Volume14
Issue number14
DOIs
StatePublished - Jul 2024

Keywords

  • expectation maximization
  • key-value data
  • LDP

Fingerprint

Dive into the research topics of 'Robust Estimation Method against Poisoning Attacks for Key-Value Data with Local Differential Privacy'. Together they form a unique fingerprint.

Cite this