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Recently, many data anonymization methods have been proposed to protect privacy in the applications of data mining. But few of them have considered the threats from user's priori knowledge of data patterns. To solve this problem, a flexible method was proposed to randomize the dataset, so that the user could hardly obtain the sensitive data even knowing data relationships in advance. The method also achieves a high level of accuracy in the mining process as demonstrated in the experiments. [ABSTRACT FROM AUTHOR]

In general-sum games, multiagent cooperation has no global objective, and only individual rationality is concerned. Agent's learning is based on the assumption of opponents' policies, and this assumption may be wrong. By defining the global objective of agents, a novel multiagent reinforcement learning algorithm was proposed. All agents selected negotiated policies during learning, and punished those agents deviating from negotiated policies to ensure the execution of these policies. It was proved that the learned Q values on each stage games converge under certain restrictions. An example was given to analyze the proven result. [ABSTRACT FROM AUTHOR]