CAJ | 학술논문

统计量化规则(SQ rule)在数据挖掘中拥有重要和有用的地位.尽管集中式挖掘SQ规则的算法已经存在,但是集中式算法不能简单应用到分布式环境中,尤其涉及到分布式环境中各方的私有信息保护的时候.考虑数据分布共享的多方,在不泄漏各自的私有信息的情况下,合作完成SQ规则的挖掘问题.该问题属于保护私有信息的数据挖掘(PPDM)研究领域的问题.基于3个PPDM的基本工具,包括安全求和、安全求平均和安全求频繁项集的集合等,提交2个算法,共同完成水平划分数据下的保护私有信息的SQ规则挖掘.其中,一个算法安全计算置信区间,该区间用来检验规则的重要性;另一个算法安全挖掘规则.最后,给出算法的正确性、安全性和复杂性分析.
통계양화규칙(SQ rule)재수거알굴중옹유중요화유용적지위.진관집중식알굴SQ규칙적산법이경존재,단시집중식산법불능간단응용도분포식배경중,우기섭급도분포식배경중각방적사유신식보호적시후.고필수거분포공향적다방,재불설루각자적사유신식적정황하,합작완성SQ규칙적알굴문제.해문제속우보호사유신식적수거알굴(PPDM)연구영역적문제.기우3개PPDM적기본공구,포괄안전구화、안전구평균화안전구빈번항집적집합등,제교2개산법,공동완성수평화분수거하적보호사유신식적SQ규칙알굴.기중,일개산법안전계산치신구간,해구간용래검험규칙적중요성;령일개산법안전알굴규칙.최후,급출산법적정학성、안전성화복잡성분석.
Statistical Quantitative(SQ)rule plays all important and useful role in data mining.Centralized algorithms have been presented for SQ rules mining.However,the algorithms cannot be easily applied to mining SQ rules on distributed data,where privacy of parties becomes great concerns.This paper considers the problem of mining SQ 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.Based on several basic tools for 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 roles;the other is to securely discover SQ rules.Besides,the analysis of the correctness,the security and the complexity of our algorithms are provided.