Evaluating the Privacy Implications of Frequent Itemset Disclosure

Abstract : Frequent itemset mining is a fundamental data analytics task. In many cases, due to privacy concerns, only the frequent itemsets are released instead of the underlying data. However, it is not clear how to evaluate the privacy implications of the disclosure of the frequent itemsets. Towards this, in this paper, we define the k-distant-IFM-solutions problem, which aims to find k transaction datasets whose pair distance is maximized. The degree of difference between the reconstructed datasets provides a way to evaluate the privacy risk. Since the problem is NP-hard, we propose a 2-approximate solution as well as faster heuristics, and evaluate them on real data.
Document type :
Conference papers
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

Contributor : Hal Ifip <>
Submitted on : Monday, November 27, 2017 - 10:31:49 AM
Last modification on : Friday, August 9, 2019 - 3:24:28 PM


 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2020-01-01

Please log in to resquest access to the document


Distributed under a Creative Commons Attribution 4.0 International License



Edoardo Serra, Jaideep Vaidya, Haritha Akella, Ashish Sharma. Evaluating the Privacy Implications of Frequent Itemset Disclosure. 32th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC), May 2017, Rome, Italy. pp.506-519, ⟨10.1007/978-3-319-58469-0_34⟩. ⟨hal-01649007⟩



Record views