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An Optimized Sanitization Approach for Minable Data Publication
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AI
DOI：10.26599/BDMA.2022.9020007.png)
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Abstract
En 中文
Minable data publication is ubiquitous since it is beneficial to sharing/trading data among commercial companies and further facilitates the development of data-driven tasks. Unfortunately, the minable data publication is often implemented by publishers with limited privacy concerns such that the published dataset is minable by malicious entities. It prohibits minable data publication since the published data may contain sensitive information. Thus, it is urgently demanded to present some approaches and technologies for reducing the privacy leakage risks. To this end, in this paper, we propose an optimized sanitization approach for minable data publication (named as SA-MDP). SA-MDP supports association rules mining function while providing privacy protection for specific rules. In SA-MDP, we consider the trade-off between the data utility and the data privacy in the minable data publication problem. To address this problem, SA-MDP designs a customized particle swarm optimization (PSO) algorithm, where the optimization objective is determined by both the data utility and the data privacy. Specifically, we take advantage of PSO to produce new particles, which is achieved by random mutation or learning from the best particle. Hence, SA-MDP can avoid the solutions being trapped into local optima. Besides, we design a proper fitness function to guide the particles to run towards the optimal solution. Additionally, we present a preprocessing method before the evolution process of the customized PSO algorithm to improve the convergence rate. Finally, the proposed SA-MDP approach is performed and verified over several datasets. The experimental results have demonstrated the effectiveness and efficiency of SA-MDP.
Keywords:
data publication
data sanitization
association rules hiding
evolutionary algorithm
Journal
IF:
6.2
Papers:
274
Citations:
1.0K