NDLI: Distributed Model Based Sampling Technique for Privacy Preserving Clustering

Content Provider	IEEE Xplore Digital Library
Author	Wu Xiao-dan Yue Dian-min Liu Feng-li Chu Chao-hsien
Copyright Year	2007
Description	Author affiliation: Hebei Univ. of Technol., Wuhan (Wu Xiao-dan; Yue Dian-min; Liu Feng-li; Chu Chao-hsien)
Abstract	The sharing of data has been proven beneficial in data mining applications. However, privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. To resolve this challenging problem, data owners must design a solution that meets privacy requirements and guarantees valid data clustering results. To achieve this dual goal, we introduce a new method for privacy-preserving clustering based on the probability distributed model for clustering. This paper proposes square wave-based clustering model, gauss distribution-based clustering model and the multivariate normal distribution-based clustering model based on the information provided by the K-means clustering result separately. And we mainly show the correctness and feasibility of the sampled data for clustering by experiment. This new preserving technique can be used not only for distributed clustering, but also for simultaneous clustering rule and data hiding.
Starting Page	192
Ending Page	197
File Size	3641149
Page Count	6
File Format	PDF
ISBN	9787560322780
DOI	10.1109/ICMSE.2007.4421846
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	HARBIN INSTITUTE OF TEC
Subject Keyword	Data privacy privacy preserving clustering Data analysis density-based clustering sampling Conference management Partitioning algorithms Data mining Technology management Engineering management Clustering algorithms distribution model Sampling methods Protection
Content Type	Text
Resource Type	Article

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