Practical and privacy-assured data indexes for outsourced cloud data

Cloud computing allows individuals and organizations outsource their data to cloud server due to the flexibility and cost savings. However, data privacy is a major concern that hampers the wide adoption of cloud services. Data encryption ensures data content confidentiality and fine-grained data access control prevents unauthorized user from accessing data. An unauthorized user may still be able to infer privacy information from encrypted data by using indexing techniques. In this paper, we investigate the problem of sensitive information leakage caused by orthogonal use of these two kinds of techniques. Based on that, we propose “core attribute”-aware techniques that can ensure privacy of outsourced data. The techniques focus on confidential attribute set of outsourced data. We adopt k-anonymity technique for the attribute indexes to prevent user from inferring privacy from unauthorized data. We formally prove the privacy-preserving guarantee of the proposed mechanism. Our extensive experiments demonstrate the practicality of the proposed mechanism, which has low computation and communication overhead.