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With the popularity of mobile wireless devices with various kinds of sensing abilities, a new service paradigm named Participatory Sensing has emerged to provide users with brand new life experience. However, the wide application of participatory sensing has its own challenges, among which privacy preservation and multimedia data participatory sensing are two critical problems. Unfortunately, none of the existing works has fully solved the problem of privacy preserving participatory sensing with multimedia data. In this paper, we propose SLICER, which is the first k-anonymous privacy preserving scheme for participatory sensing with multimedia data. SLICER integrates a data coding technique and message exchanging strategies, to achieve strong protection of participants' privacy, while maintaining high data accuracy. In addition, two slice transferring strategies are well designed for slice transfer to minimize the total transfer cost. Finally, we have implemented SLICER and evaluated its performance using publicly released taxi traces. Our evaluation results show that SLICER achieves high data accuracy, with low computation and communication overhead.

With the popularity of cloud computing, many companies would outsource their social network data to a cloud service provider, where privacy leaks have become a more and more serious problem. However, most of the previous studies have ignored an important fact, i.e., in real social networks, users possess various attributes and have the flexibility to decide which attributes of their profiles are sensitive attributes by themselves. These sensitive attributes of the users should be protected from being revealed when outsourcing a social network to a cloud service provider. In this paper, we consider the problem of resisting privacy attacks with neighborhood information of both network structure and labels of one-hop neighbors as background knowledge. To tackle this problem, we propose a Global Similarity-based Group Anonymization (GSGA) method to generate a anonymized social network while maintaining as much utility as possible. We also extensively evaluate our approach on both real data set and synthetic data sets. Evaluation results show that the social network anonymized by our approach can still be used to answer aggregation queries with high accuracy.