Title

SecDM: Privacy-Preserving Data Outsourcing Framework with Differential Privacy

Document Type

Article

Publication Date

5-2020

Abstract

Data-as-a-service (DaaS) is a cloud computing service that emerged as a viable option to businesses and individuals for outsourcing and sharing their collected data with other parties. Although the cloud computing paradigm provides great flexibility to consumers with respect to computation and storage capabilities, it imposes serious concerns about the confidentiality of the outsourced data as well as the privacy of the individuals referenced in the data. In this paper we formulate and address the problem of querying encrypted data in a cloud environment such that query processing is confidential and the result is differentially private. We propose a framework where the data provider uploads an encrypted index of her anonymized data to a DaaS service provider that is responsible for answering range count queries from authorized data miners for the purpose of data mining. To satisfy the confidentiality requirement, we leverage attribute-based encryption to construct a secure kd-tree index over the differentially private data for fast access. We also utilize the exponential variant of the ElGamal cryptosystem to efficiently perform homomorphic operations on encrypted data. Experiments on real-life data demonstrate that our proposed framework preserves data utility, can efficiently answer range queries, and is scalable with increasing data size.

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