Privacy Vulnerability of NeNDS Collaborative Filtering.

Eyal Nussbaum, Michael Segal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Many of the data we collect today can easily be linked to an individual, household or entity. Unfortunately, using data without protecting the identity of the data owner can lead to data leaks and potential lawsuits. To maintain user privacy when a publication of data occurs many databases employ anonymization techniques, either on the query results or the data itself. In this paper we examine variant of such technique, “data perturbation” and discuss its vulnerability. The data perturbation method deals with changing the values of records in the dataset while maintaining a level of accuracy over the resulting queries. We focus on a relatively new data perturbation method called NeNDS [1] and show a possible partial knowledge privacy attack on this method.
Original languageEnglish
Title of host publicationCyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings
EditorsShlomi Dolev, Oded Margalit, Benny Pinkas, Alexander Schwarzmann
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Electronic)978-3-030-78086-9
ISBN (Print)9783030780852
StatePublished - 2021
Event5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021 - Be'er Sheva, Israel
Duration: 8 Jul 20219 Jul 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12716 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021
CityBe'er Sheva


  • Collaborative filtering
  • Privacy
  • NeNDS

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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