vMixing coins strategy can realize the anonymity of user information, thereby protecting the user's privacy. Ideally, the blacklist is public information and all bad coins are recorded in it. However, due to the failure of some bad

Parties are expected to be cooperative such that some tasks, e.g. two-party computation in social cloud, become much easier. It is well known that reputation is an important property to promote cooperation among parties in game theory. Therefore, we consider the effect of reputation when parties interact in social cloud to find a new way realizing mutual cooperation. More specifically, parities in the social cloud are rational who value their reputation. Cooperation can boost their reputation, so they have incentives to cooperate with others such that they may get a higher utility. The basic idea of this paper is to add reputation deriving from social cloud as part of the utility. That is, we describe the architecture and interaction between two rational parties in the social cloud, where two parties receive their opponent's trust or reputation from the social cloud. The computation of trust and reputation is finished in the social cloud. Finally, we prove that given proper parameters in rational secure two-party computation (rational STPC), it is possible to complete the computation in just one round in the second stage of the protocol.

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Purpose The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms. Design/methodology/approach Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods. Findings The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M. Originality/value This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.