Privacy Vulnerability of NeNDS Collaborative Filtering

Eyal Nussbaum; Michael Segal

doi:10.1007/978-3-030-78086-9_11

Privacy Vulnerability of NeNDS Collaborative Filtering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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 language	English
Title of host publication	Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings
Editors	Shlomi Dolev, Oded Margalit, Benny Pinkas, Alexander Schwarzmann
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	145-152
Number of pages	8
ISBN (Print)	9783030780852
DOIs	https://doi.org/10.1007/978-3-030-78086-9_11
State	Published - 1 Jan 2021
Event	5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021 - Be'er Sheva, Israel Duration: 8 Jul 2021 → 9 Jul 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12716 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021
Country/Territory	Israel
City	Be'er Sheva
Period	8/07/21 → 9/07/21

Keywords

Collaborative filtering
NeNDS
Privacy

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-78086-9_11

Cite this

Nussbaum, E., & Segal, M. (2021). Privacy Vulnerability of NeNDS Collaborative Filtering. In S. Dolev, O. Margalit, B. Pinkas, & A. Schwarzmann (Eds.), Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings (pp. 145-152). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12716 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-78086-9_11

Nussbaum, Eyal ; Segal, Michael. / Privacy Vulnerability of NeNDS Collaborative Filtering. Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. editor / Shlomi Dolev ; Oded Margalit ; Benny Pinkas ; Alexander Schwarzmann. Springer Science and Business Media Deutschland GmbH, 2021. pp. 145-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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Nussbaum, E & Segal, M 2021, Privacy Vulnerability of NeNDS Collaborative Filtering. in S Dolev, O Margalit, B Pinkas & A Schwarzmann (eds), Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12716 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 145-152, 5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021, Be'er Sheva, Israel, 8/07/21. https://doi.org/10.1007/978-3-030-78086-9_11

Privacy Vulnerability of NeNDS Collaborative Filtering. / Nussbaum, Eyal; Segal, Michael.
Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. ed. / Shlomi Dolev; Oded Margalit; Benny Pinkas; Alexander Schwarzmann. Springer Science and Business Media Deutschland GmbH, 2021. p. 145-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12716 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Nussbaum E, Segal M. Privacy Vulnerability of NeNDS Collaborative Filtering. In Dolev S, Margalit O, Pinkas B, Schwarzmann A, editors, Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 145-152. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-78086-9_11

Privacy Vulnerability of NeNDS Collaborative Filtering

Abstract

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Keywords

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