Restricted Sensitive Attributes-based Sequential Anonymization (RSA-SA) approach for privacy-preserving data stream publishing

Abstract

Data streams have become a widely-adopted data representation format in many real-world applications. This data streaming may be published for different scientific research, mining, or analysis purposes. However, such streams may contain personal-specific data that could be considered as sensitive about individuals. This makes the privacy preserving of data streams against privacy disclosure attacks, while maintaining their utilization, is a real challenge. Some studies have considered privacy-preserving publishing over data streams with only Single Sensitive Attribute, in which they do not protect the published streams from all possible privacy attacks. In this paper, we propose a novel Restricted Sensitive Attributes-based Sequential Anonymization (RSA-SA) approach for privacy-preserving data stream publishing. Besides, two new privacy restrictions are introduced to restrict the published Sensitive Attributes values: Semantic-diversity and Sensitivity-diversity. RSA-SA can protect the sensitive values of the published data streams against the related privacy attacks, including the attribute disclosure, skewness, similarity, and sensitivity attacks. In addition, RSA-SA handles data streams that have either single or multiple sensitive attributes with minimum information loss and delay time. Thus, the data utility of the published data streams is efficiently maintained to provide more accurate mining and analytical results, where robust invulnerability to privacy attacks is sustained.