Weiwei Jing

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When data mining occurs on distributed data, privacy of parties becomes great concerns. This paper considers the problem of mining quantitative association rules without revealing the private information of parties who compute jointly and share distributed data. The issue is an area of privacy preserving data mining (PPDM) research. Some researchers have(More)
Statistics measurements are of great importance in data set description. Although there have been some papers about statistical analysis, little work focused on the flavors of measurements or privacy-preserving property. In this paper, we consider the applications of secure multi-party computation technology in statistics measurements computation to(More)
The proliferation of the network has opened up great opportunities for cooperative computation. But privacy concerns often prevent different parties from sharing their data in order to do cooperative computation tasks. Secure multi-party computation deals with the privacy concern in cooperative computation while ensuring correctness of the computation and(More)
This paper considers the problem of mining Statistical Quantitative rules (SQ rules) without revealing the private information of parties who compute jointly and share distributed data. Based on several basic tools for Privacy-Preserving Data Mining (PPDM), including secure sum, secure mean and secure frequent itemsets, this paper presents two algorithms to(More)
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