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Hiding sensitive frequent itemsets by item removal via two-level multi-objective optimization

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Abstract

Privacy Preserving Data Mining (PPDM) is an important research area in data mining, which aims at protecting the privacy during the data mining process so that personal data and sensitive information is not revealed to unauthorized persons. PPDM is a critical task as data often contain sensitive information about individuals that can easily compromise their privacy such as their financial status, political beliefs or medical history. In particular, many algorithms were proposed to hide sensitive frequent itemsets (values that frequently co-occur) in a transaction database. However, a major problem is that those algorithms are based on removing entire transactions (records) instead of single items (values). Hence, many changes are made to databases, which lead to losing useful information. To avoid making too many changes to a database while still preserving privacy, this paper proposes a novel PPDM algorithm called NSGAII4ID for hiding sensitive frequent itemsets by only removing some items from transactions rather than removing whole transactions. To our best knowledge, this is the first paper exploring this approach. Sanitization is treated as a multi-objective optimization problem where the goal is to reduce four side effects, namely hiding failure, missing cost, artificial cost, and database dissimilarity. However, we observe that only three side effects matter in our case (the artificial cost is always zero). Besides, another key difference is that NSGAII4ID sanitizes a database by performing optimization at two levels. At the transaction level, a multi-objective optimization algorithm is applied to find a subset of candidate transactions to be modified. Then, at the item level, NSGAII4ID searches for an optimal subset of items to be removed from each candidate transaction. We show that this problem is exactly the Set cover Problem (SCP) which we solve by using a fast greedy polynomial algorithm. We have conducted extensive experiments on four datasets to compare NSGAII4DT with state-of-the-art PPDM algorithms in terms of runtime, memory cost and total number of removed items during sanitization. The obtained results show that NSGAII4DT achieves a very good balance in minimizing side effects compared with the state-of-the art algorithms.

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Notes

  1. For transactions having TID = 2 and TID = 5, the GSCA could have returned another minimal set of victim items which is {i3}.

  2. the source code of our algorithm is available on the Github at the following address: https://github.com/mira34/NSGAII4ID-algorithm

  3. The generator may be donwloaded at : https://sourceforge.net/projects/ibmquestdatagen/

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Authors and Affiliations

  1. Department of Computer Science, University of Bordj Bou Arreridj, Bordj Bou Arreridj, Algeria

    Mira Lefkir & Farid Nouioua

  2. LIS, UMR-CNRS 7020, Aix-Marseille University, Marseille, France

    Farid Nouioua

  3. Shenzhen University, Shenzhen, China

    Philippe Fournier-Viger

Authors
  1. Mira Lefkir
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  2. Farid Nouioua
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  3. Philippe Fournier-Viger
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Correspondence to Philippe Fournier-Viger.

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Lefkir, M., Nouioua, F. & Fournier-Viger, P. Hiding sensitive frequent itemsets by item removal via two-level multi-objective optimization. Appl Intell 53, 10027–10052 (2023). https://doi.org/10.1007/s10489-022-03808-6

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