@inproceedings{e22458c1813044ec8ac814c7154fa3d7,
title = "Privacy Vulnerability of NeNDS Collaborative Filtering",
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.",
keywords = "Collaborative filtering, NeNDS, Privacy",
author = "Eyal Nussbaum and Michael Segal",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021 ; Conference date: 08-07-2021 Through 09-07-2021",
year = "2021",
month = jan,
day = "1",
doi = "https://doi.org/10.1007/978-3-030-78086-9_11",
language = "American English",
isbn = "9783030780852",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "145--152",
editor = "Shlomi Dolev and Oded Margalit and Benny Pinkas and Alexander Schwarzmann",
booktitle = "Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings",
address = "Germany",
}