STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation

Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual's private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution. This work addresses the problem of PPDM by proposing statistical transformation with intuitionistic fuzzy (STIF) algorithm for data perturbation. The STIF algorithm contains statistical methods weight of evidence, information value and intuitionistic fuzzy Gaussian membership function. The STIF algorithm is applied on three benchmark datasets adult income, bank marketing and lung cancer. The classifier models decision tree, random forest, extreme gradient boost and support vector machines are used for accuracy and performance analysis. The results show that the STIF algorithm achieves 99% of accuracy for adult income dataset and 100% accuracy for both bank marketing and lung cancer datasets. Further, the results highlights that the STIF algorithm outperforms in data perturbation capacity and privacy preserving capacity than the state-of-art algorithms without any information loss on both numerical and categorical data.

Privacy Preserving Association Rule Mining on Distributed Healthcare Data: COVID-19 and Breast Cancer Case Study

Association rule mining can be used in healthcare data mining to provide solutions to life-threatening diseases like recent COVID-19. Due to healthcare data privacy concerns, privacy preserving distributed healthcare data mining becomes the primary focus of medical science research. Recently, Chahar et al. (Sādhanā 42:1997–2007, 2017) proposed privacy preserving distributed association rule mining scheme with insecure communication channels. They used the concept of an elliptic curve-based paillier cryptosystem to achieve privacy, authenticity, and integrity. We observed some security vulnerabilities in their privacy preserving association rule mining scheme when implemented with insecure communication channels. We observed that the security vulnerabilities will result in the disclosure of private data of sites (or participants). Furthermore, we propose a secure version of their scheme to solve the security vulnerabilities with insecure communication channels. Theoretical and experimental analysis shows that the proposed scheme has almost equal computation and communication complexities with better securities. A case study on the effectiveness of the proposed approach in combating COVID-19 coronavirus and Breast Cancer is also discussed.