Secure Data Outsourcing with Adversarial Data Dependency Constraints
Document Type
Conference Proceeding
Publication Date
6-30-2016
Abstract
Cloud computing enables end-users to outsource their dataset and data management needs to a third-party service provider. One of the major security concerns of the outsourcing paradigm is how to protect sensitive information in the outsourced dataset. In general, the sensitive information can be protected by encryption. However, data dependency constraints in the outsourced data may serve as adversary knowledge and bring security vulnerabilities. In this paper, we focus on functional dependency (FD), an important type of data dependency constraints, and study the security threats by the adversarial FDs. We design the practical scheme that can defend against the FD attack by encrypting a small amount of non-sensitive data (encryption overhead). We prove that searching for the scheme that leads to the optimal encryption overhead is NP-complete, and design efficient heuristic algorithms. We conduct an extensive set of experiments on two real-world datasets. The experiment results show that our heuristic approach brings small amounts of encryption overhead (at most 1% more than the optimal overhead), and enjoys a ten times speedup compared with the optimal solution. Besides, our approach can reduce up to 90% of the encryption overhead of the-state-of-art solution.
DOI
10.1109/BigDataSecurity-HPSC-IDS.2016.17
Montclair State University Digital Commons Citation
Dong, Boxiang; Wang, Wendy; and Yang, Jie, "Secure Data Outsourcing with Adversarial Data Dependency Constraints" (2016). Department of Computer Science Faculty Scholarship and Creative Works. 528.
https://digitalcommons.montclair.edu/compusci-facpubs/528