An evolutionary computation-based sensitive pattern hiding model under a multi-threshold constraint in healthcare

In the domain of collaborative frequent pattern mining, the preservation of privacy has emerged as a critical area of investigation as the data procured by the analytical arm of business organizations may contain critical sensitive patterns. Further, this investigation may cause the disclosure of sensitive information. Various Evolutionary techniques have been proposed in the past to efficiently investigate such sensitive patterns while preserving data privacy. These techniques utilized various nature-inspired evolutionary-based algorithms like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for masking such confidential information before sharing data to the business organizations. However, most of them either choose to delete entire sensitive transactions for masking confidential information or by selecting a victim item and its subsequent deletion based on a single parameter such as the length or frequency of a sensitive item. This may cause various side effects, and hence reduces the utility of sanitized datasets. In this paper, we propose a novel and innovative Particle Swarm Optimization (PSO) based algorithm specifically designed to address the challenge of concealing sensitive patterns within the constraints of a multi-threshold framework. The proposed algorithm emphasizes enhancing the utility of sanitized datasets by selecting victim items based on multiple parameters, unlike the schemes proposed in the past. For making the scheme suitable for real applications, we propose to introduce the dynamic multithreshold-based framework, the algorithm utilizes the bi-variate normal distribution to determine the dynamic threshold value for each sensitive item set. This helps in improving the utility of the underlying dataset while making lesser modifications for hiding sensitive knowledge. The empirical findings substantiate the effectiveness of the proposed algorithm in concealing sensitive patterns over benchmark FIMI datasets, Heart Disease, and Heart Attack Prediction datasets. The experimental results show the superiority of the proposed scheme by reducing the side-effect with minimum loss of data over the existing PSO and ACO-based algorithms. We noted that the Failure-to-Hide(FTH) named side effect is significantly lower in most of the cases for our algorithm in comparison to the existing algorithms.