Privacy-Preserving Frequent-Itemset Mining of Data Streams

• Main Authors: Wen-chung Wu, 吳文群
• Other Authors: Ching-Ming Chao
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/8s4by4

碩士 === 東吳大學 === 資訊科學系 === 96 === Compared to traditional static databases, data streams have the following characteristics: (1) Data flows in with fast speed; (2) The amount of data is enormous; (3) Data distribution changes constantly with time; (4) Immediate response is required. Due to the emergence of this new type of data, data stream mining has recently become a very popular research issue. There have been many studies proposing efficient mining algorithms for data streams. However, to the best of out knowledge, there is no research that studies the privacy preservation issue of data stream mining. In this paper, we propose a method for privacy-preserving frequent-itemset mining of data streams. We not only use the “data integration” technique to combine items of the database for data security, but also use the “incremental frequent itemsets mining” algorithm designed by the sliding-window model to proceed fast frequent itemsets mining. The experiment result shows that our method can solve the problem of privacy-preserving frequent-itemset mining of data streams. It keeps the characteristics of the fast mining, privacy preservation and efficiency.