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The Scientific World Journal
Volume 2014 (2014), Article ID 581426, 9 pages
http://dx.doi.org/10.1155/2014/581426
Research Article

Preserving Differential Privacy for Similarity Measurement in Smart Environments

School of Computer Science and Engineering, Soongsil University, Information Science Building, Sangdo-dong, Dongjak-gu, Seoul 156-743, Republic of Korea

Received 5 April 2014; Accepted 24 June 2014; Published 15 July 2014

Academic Editor: Jong-Hyuk Park

Copyright © 2014 Kok-Seng Wong and Myung Ho Kim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Advances in both sensor technologies and network infrastructures have encouraged the development of smart environments to enhance people’s life and living styles. However, collecting and storing user’s data in the smart environments pose severe privacy concerns because these data may contain sensitive information about the subject. Hence, privacy protection is now an emerging issue that we need to consider especially when data sharing is essential for analysis purpose. In this paper, we consider the case where two agents in the smart environment want to measure the similarity of their collected or stored data. We use similarity coefficient function as the measurement metric for the comparison with differential privacy model. Unlike the existing solutions, our protocol can facilitate more than one request to compute without modifying the protocol. Our solution ensures privacy protection for both the inputs and the computed results.