Location-Based Services (LBSs) are playing an increasingly important role in people’s daily activities nowadays. While enjoying the convenience provided by LBSs, users may lose privacy since they report their personal information to the untrusted LBS server. Although many approaches have been proposed to preserve users’ privacy, most of them just focus on the user’s location privacy, but do not consider the query privacy. Moreover, many existing approaches rely heavily on a trusted third-party (TTP) server, which may suffer from a single point of failure. To solve the problems above, in this paper we propose a Cache-Based Privacy-Preserving (CBPP) solution for users in LBSs. Different from the previous approaches, the proposed CBPP solution protects location privacy and query privacy simultaneously, while avoiding the problem of TTP server by having users collaborating with each other in a mobile peer-to-peer (P2P) environment. In the CBPP solution, each user keeps a buffer in his mobile device (e.g., smartphone) to record service data and acts as a micro TTP server. When a user needs LBSs, he sends a query to his neighbors first to seek for an answer. The user only contacts the LBS server when he cannot obtain the required service data from his neighbors. In this way, the user reduces the number of queries sent to the LBS server. We argue that the fewer queries are submitted to the LBS server, the less the user’s privacy is exposed. To users who have to send live queries to the LBS server, we employ the l-diversity, a powerful privacy protection definition that can guarantee the user’s privacy against attackers using background knowledge, to further protect their privacy. Evaluation results show that the proposed CBPP solution can effectively protect users’ location and query privacy with a lower communication cost and better quality of service.
Smart grid is a network of computers and power infrastructures that monitor and control energy usage by collecting data from the power grid. It can gather and distribute information about the behavior of all consumers in order to improve the efficiency, reliability, economics, safety, and sustainability of electricity services. In this paper, we propose a self-certi�ed P�C-based privacypreserving data aggregation scheme in smart grid to increase computation efficiency and achieve privacy protection of end users. To realize the anonymous aggregation of multidimensional data, we adopt the Chinese Remainder eorem and homomorphic property of Paillier cryptosystem to achieve it. Comparing our scheme with Lu et al. 's scheme, the result shows that our scheme has more advantages over Lu et al. 's scheme in terms of computational costs of the user, GW, and OA. A�er adopting batch veri�cation technique, the computational cost of GW is constant in our scheme, however, that of GW is linear with the number of the users in Lu et al. 's scheme. Furthermore, our scheme also supports the anonymity of the user's identity. It indicates that the local gateway GW does not know the real identity of the resident user such that the privacy of the user is better protected.
With the growing popularity of cloud computing in recent years, data owners (DOs) now prefer to outsource their data to cloud servers and allow the specific data users (DUs) to retrieve the data. Searchable encryption is an important tool to provide secure search over the encrypted cloud data without infringing data confidentiality and data privacy. In this work, we consider a secure search service providing fine-grained and search functionality, called attribute-based multiple keyword search (ABMKS), which can be seen as an extension of searchable encryption. In the existing ABMKS schemes, the computation operations in the encrypted keyword index generation are time-consuming modular exponentiation, and the number of which is linearly growing with the factor m. Here m is the number of keywords embedded in a file. To reduce the computation overhead, in this paper, we propose an ABMKS with only multiplication operations in encrypted keyword index generation. As a result, the computation cost of the encrypted keyword index generation is more efficient than the existing schemes. In addition, the encrypted keyword indexes are aggregated into one item, which is regardless of the number of underlying keywords in a file data. Finally, the security and the performance analysis demonstrate that our scheme is both efficient and secure.
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