Cluster-based anonymity model and algorithm for 1:1 dataset with a single sensitive attribute using machine learning technique

Abstract

Privacy is a significant issue that requires consideration in all applications. Data collected from various individuals and organizations must be disclosed to the public or private parties for analysis and research purposes. The collected data are studied and analyzed digitally for the extraction of various useful patterns for decision-making research purposes. Privacy-preserving data publishing is significant as privacy violations in the patient’s data may have an adverse effect on the individual positive reputation. An efficient Cluster Based anonymity model has been proposed to anonymizes the 1:1 dataset with a single sensitive attribute through the introduction of a concept named “Semi-sensitive attribute.” Based on correlation, the attributes are categorized as quasi-identifier and semi-sensitive attributes. The k-anonymity is implemented on the quasi-identifier with the semi-sensitive attribute table and Fuzzy c-means clustering has been implemented to fix a range of values for anonymizing the semi-sensitive attributes. The disease is considered a sensitive attribute as the research work focuses on the medical dataset. The proposed model is demonstrated to resist the three privacy attacks such as, i)Identity Disclosure, ii) Attribute Disclosure, and iii) Membership Disclosure. The utility loss is calculated for each row and utility loss of each record are aggregated and considered as the total information loss for each attribute. Cluster Based anonymity model measured the utility loss for all the attributes and the average utility loss for the anonymized patient dataset is 3.78%.

Keywords

• Privacy-preserving
• Semi-sensitive attribute
• Fuzzy c-means clustering
• Identity disclosure
• Attribute disclosure
• Membership disclosure