An efficient and robust privacy protection technique for massive streaming choice-based information
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In participatory sensing system community people contribute information to be shared by everybody. However, none would be tolerant enough to contribute voluntarily if her privacy is not protected. This has evoked the idea of research in the area of preserving privacy in participatory sensing system. On the other hand, data integrity is desired imperatively to make the service trustworthy and user-friendly. In this paper, we have investigated the performance of a greedy algorithm and its randomized variant to achieve an acceptable tradeoff between these two orthogonal key parameters. We have also analyzed the ability of a third party adversary to decode privacy-sensitive data by eavesdropping. Our experimental results show that the proposed method is performing satisfactorily as an approach of balancing user privacy and data integrity.