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The solution of the contradiction between privacy protection and data utility is a research hotspot in the field of privacy protection. Aiming at the problem of tradeoff between privacy and utility in the scenario of differential privacy offline data release, the optimal differential privacy mechanism is studied by using the rate distortion theory. Firstly, based on Shannon communication theory, the noise channel model of differential privacy is abstracted, and the mutual information and the distortion function is used to measure the privacy and utility of data publishing, and the optimization model based on rate distortion theory is constructed. Secondly, considering the influence of associated auxiliary background knowledge on mutual information privacy leakage, a mutual information privacy measure based on joint events is proposed, and a minimum privacy leakage model is proposed by modifying the rate distortion function. Finally, aiming at the difficulty in solving the Lagrange multiplier method, an approximate algorithm for solving the mutual information privacy optimization channel mechanism is proposed based on the alternating iterative method. The effectiveness of the proposed iterative approximation method is verified by experimental simulation. At the same time, the experimental results show that the proposed method reduces the mutual information privacy leakage under the condition of limited distortion, and improves the data utility under the same privacy tolerance

With the development of cloud services, many network fraudulent incidents have damaged the interests of transaction subjects and brought a great crisis to online service. To analyze and solve the key conditions for different participants to achieve mutual trust and win-win situations in the cloud computing, we propose a trust prediction game model composed of the supervisor, cloud service provider (CSP), and user. Firstly, we use the Bayesian formula to predict the behavior trust attribute and propose the trust service map function and weight algorithm by the principle of maximum dispersion. Secondly, based on the perspective of the user, we establish a two-party dynamic game model and discuss the existence possibility of game equilibrium. Thirdly, based on the perspective of CSP, we establish a tripartite game model, analyze several possible equilibria, and obtain the equilibrium strategy from a mixed perspective. Finally, the experimental results show that the supervision has a significant impact on the strategy selection of the players, verify the correctness and effectiveness of various game parameters, exhibition the better advantage than several articles under some different standards, and make a useful exploration for building a secure and trusted cloud service environment.

With the development of cloud computing, privacy security issues have become increasingly prominent, which is of concern to industry and academia. We review the research progress on privacy security issues from the perspective of several privacy security protection technologies in cloud computing. First, we introduce some privacy security risks of cloud computing and propose a comprehensive privacy security protection framework. Second, we show and discuss the research progress of several technologies, such as access control; ciphertext policy attribute-based encryption (CP-ABE); key policy attribute-based encryption (KP-ABE); the fine-grain, multi-authority, revocation mechanism; the trace mechanism; proxy re-encryption (PRE); hierarchical encryption; searchable encryption (SE); and multi-tenant, trust, and a combination of multiple technologies, and then compare and analyze the characteristics and application scope of typical schemes. Last, we discuss current challenges and highlight possible future research directions.