Considering the untrusted server, differential privacy and local differential privacy has been used for privacy-preserving in data aggregation. Through our analysis, differential privacy and local differential privacy cannot achieve Nash equilibrium between privacy and utility for mobile service based multiuser collaboration, which is multiuser negotiating a desired privacy budget in a collaborative manner for privacy-preserving. To this end, we proposed a Privacy-Preserving Data Aggregation Framework (PPDAF) that reached Nash equilibrium between privacy and utility. Firstly, we presented an adaptive Gaussian mechanism satisfying Nash equilibrium between privacy and utility by multiplying expected utility factor with conditional filtering noise under expected privacy budget. Secondly, we constructed PPDAF using adaptive Gaussian mechanism based on negotiating privacy budget with heuristic obfuscation. Finally, our theoretical analysis and experimental evaluation showed that the PPDAF could achieve Nash equilibrium between privacy and utility. Furthermore, this framework can be extended to engineering instances in a data aggregation setting
In the cloud-based vehicular ad-hoc network (VANET), massive vehicle information is stored on the cloud, and a large amount of data query, calculation, monitoring, and management are carried out at all times. The secure spatial query methods in VANET allow authorized users to convert the original spatial query to encrypted spatial query, which is called query token and will be processed in ciphertext mode by the service provider. Thus, the service provider learns which encrypted records are returned as the result of a query, which is defined as the access pattern. Since only the correct query results that match the query tokens are returned, the service provider can observe which encrypted data are accessed and returned to the client when a query is launched clearly, and it leads to the leakage of data access pattern. In this paper, a reconstruction attack scheme is proposed, which utilizes the access patterns in the secure query processes, and then it reconstructs the index of outsourced spatial data that are collected from the vehicles. The proposed scheme proves the security threats in the VANET. Extensive experiments on real-world datasets demonstrate that our attack scheme can achieve quite a high reconstruction rate.
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