Evaluating the Privacy Implications of Frequent Itemset Disclosure

Edoardo Serra; Jaideep Vaidya; Haritha Akella; Ashish Sharma

doi:10.1007/978-3-319-58469-0_34

Evaluating the Privacy Implications of Frequent Itemset Disclosure

Edoardo Serra, Jaideep Vaidya, Haritha Akella, Ashish Sharma

Computer Science Department

Research output: Chapter in Book/Report/Conference proceeding › Chapter

1 Scopus citations

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.

Original language	American English
Title of host publication	IFIP International Conference on ICT Systems Security and Privacy Protection
DOIs	https://doi.org/10.1007/978-3-319-58469-0_34
State	Published - 1 Jan 2017

Keywords

column generation
inverse frequent itemset mining

EGS Disciplines

Computer Sciences

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https://doi.org/10.1007/978-3-319-58469-0_34

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title = "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.",

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AU - Akella, Haritha

AU - Sharma, Ashish

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N2 - 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.

AB - 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.

KW - column generation

KW - inverse frequent itemset mining

UR - https://scholarworks.boisestate.edu/cs_facpubs/107

UR - https://doi.org/10.1007/978-3-319-58469-0_34

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DO - 10.1007/978-3-319-58469-0_34

M3 - Chapter

BT - IFIP International Conference on ICT Systems Security and Privacy Protection

ER -

Evaluating the Privacy Implications of Frequent Itemset Disclosure

Abstract

Keywords

EGS Disciplines

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