privacy-preserving statistical quantitative rules mining
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 accomplish privacy-preserving SQ rules mining over horizontally partitioned data. One is to securely compute confidence intervals for testing the significance of rules; the other is to securely discover SQ rules.
statistical quantitative rules privacy-preserving data mining