Privacy preserving access control for third-party data management systems
Abstract
The tremendous growth in electronic media has made publication of information in either open or closed environments easy and effective. However, most application domains (e.g. electronic health records (EHRs)) require that the fine-grained selective access to information be enforced in order to comply with legal requirements, organizational policies, subscription conditions, and so forth. The problem becomes challenging with the increasing adoption of cloud computing technologies where sensitive data reside outside of organizational boundaries. An important issue in utilizing third party data management systems is how to selectively share data based on fine-grained attribute based access control policies and/or expressive subscription queries while assuring the confidentiality of the data and the privacy of users from the third party. In this thesis, we address the above issue under two of the most popular dissemination models: pull based service model and subscription based publish-subscribe model. Encryption is a commonly adopted approach to assure confidentiality of data in such systems. However, the challenge is to support fine grained policies and/or expressive content filtering using encryption while preserving the privacy of users. We propose several novel techniques, including an efficient and expressive group key management scheme, to overcome this challenge and construct privacy preserving dissemination systems.
Degree
Ph.D.
Advisors
Bertino, Purdue University.
Subject Area
Computer science
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