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Statistical limitations of sensitive itemset hiding methods

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Abstract

Frequent Itemset Hiding has long been an area of study for privacy-preserving data mining. The goal is to alter a dataset so that it may be released without revealing particular sensitive aggregates (e.g., frequent itemsets or association rules.) Typically the approach is to remove items from transactions to reduce the support of the sensitive itemset(s) below a threshold, while minimizing the changes or impact on other frequent itemsets. In this paper, we ask if such hiding can be discovered: Do hiding methods lead to anomalies that suggest that a sensitive itemset likely existed in the dataset, and has been hidden? We show that a suppressed sensitive itemset may behave like an outlier among its neighboring itemsets after suppression, indicating that the dataset is likely altered. KL-divergence and \(\chi ^2\)-divergence are used to calculate the difference between expected and actual probability distributions of itemsets for observing anomalous behavior. Experimental results on four datasets show that suppressed sensitive itemsets often stand out as the most significant outlier in many cases, irrespective of the victim item selection method. We propose two defensive approaches that counter this attack.

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Data availability and access

The datasets used during the current study are available on the GitHub platform link https://github.com/ShaliniJangra/Attack_Defense and online repository https://www.philippe-fournier-viger.com/spmf/index.php?link=datasets.php.

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Funding

This work is funded by Science and Engineering Research Board (SERB), a statutory body under the Department of Science and Technology (DST), Government of India.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, IIT Roorkee, Uttarakhand, 247667, Roorkee, India

    Jangra Shalini & Toshniwal Durga

  2. Department of Computer Science and CERIAS, Purdue University, West Lafayette, IN, 47907-2107, USA

    Jangra Shalini & Clifton Chris

Authors
  1. Jangra Shalini
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  2. Toshniwal Durga
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  3. Clifton Chris
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Contributions

Shalini Jangra: Conception and design of study, Acquisition of data, Coding and Implementation, Analysis and interpretation of Results, Writing- Original draft preparation, Review & Editing, Funding acquisition Durga Toshniwal: Review & Editing, Supervision Chris Clifton: Conceptualization, Methodology, Analysis and interpretation of Results, Review & Editing, Supervision

Corresponding author

Correspondence to Jangra Shalini.

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The authors have no competing interest to declare that are relevant to the content of this article.

Ethical and informed consent for data used

The work uses publicly available and synthetically generated datasets which do not have any identifiable information. No ethical approval was needed.

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Shalini, J., Durga, T. & Chris, C. Statistical limitations of sensitive itemset hiding methods. Appl Intell 53, 24275–24292 (2023). https://doi.org/10.1007/s10489-023-04781-4

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