Experimental analysis of privacy-preserving statistics computation

Hiranmayee Subramaniam, Rebecca N. Wright, Zhiqiang Yang

Research output: Chapter in Book/Report/Conference proceedingChapter

18 Scopus citations

Abstract

The recent investigation of privacy-preserving data mining and other kinds of privacy-preserving distributed computation has been motivated by the growing concern about the privacy of individuals when their data is stored, aggregated, and mined for information. Building on the study of selective private function evaluation and the efforts towards practical algorithms for privacy-preserving data mining solutions, we analyze and implement solutions to an important primitive, that of computing statistics of selected data in a remote database in a privacypreserving manner. We examine solutions in different scenarios ranging from a high speed communications medium, such as a LAN or highspeed Internet connection, to a decelerated communications medium to account for worst-case communication delays such as might be provided in a wireless multihop setting. Our experimental results show that in the absence of special-purpose hardware accelerators or practical optimizations, the computational complexity is the performance bottleneck of these solutions rather than the communication complexity. We also evaluate several practical optimizations to amortize the computation time and to improve the practical efficiency.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsWillem Jonker, Milan Petkovic
PublisherSpringer Verlag
Pages55-66
Number of pages12
ISBN (Print)3540229833, 9783540229834
DOIs
StatePublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3178

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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