New models and techniques on privacy-preserving information sharing

Due to the wide deployment of Internet and information technology, the ever growing privacy concern has been a major obstacle for information sharing. This thesis work thus centres on developing new models and techniques to deal with emerging privacy issues in various contexts of information sharing and exchange. Specifically, along with the main theme, this thesis work can be divided into three categories, summarized as follows. Problem #1 Privacy-preserving data mining spanning multiple private data sources. The goal of this research is to enable the computation as the data collected in a central place, but preserve the privacy of participating sites. This problem has been studied in the context of classification with multiple private data sources integrated with join semantics. Problem #2 Privacy-preserving data publishing. This research aims to address the scenario where a data owner wishes to publish the data while preserving individual privacy. This topic has been extensively studied in the context of relational data, but much less is known for transaction data. We propose one way to address this issue in this thesis. Problem #3 Privacy-enhancing online personalized service. This research starts from an end user’s point of view, and studies how to submit a piece of personal data to exchange for service without compromising individual privacy. Our contribution on this topic is a framework under which individual users can strike a balance between service quality and privacy protection.