PraDa: Privacy-Preserving Data-Deduplication-As-a-Service
The data-cleaning-as-a-service (DCaS) paradigm enables users to outsource their data and data cleaning needs to computationally powerful third-party service providers. It raises several security issues. One of the issues is how the client can protect the private information in the outsourced data. In this paper, we focus on data deduplication as the main data cleaning task, and design two efficient privacy-preserving data-deduplication methods for the DCaS paradigm. We analyze the robustness of our two methods against the attacks that exploit the auxiliary frequency distribution and the knowledge of the encoding algorithms. Our empirical study demonstrates the efficiency and effectiveness of our privacy preserving approaches.
MSU Digital Commons Citation
Dong, Boxiang; Liu, Ruilin; and Wang, Wendy Hui, "PraDa: Privacy-Preserving Data-Deduplication-As-a-Service" (2014). Department of Computer Science Faculty Scholarship and Creative Works. 484.