Privacy-Preserving Clustering Using Representatives over Arbitrarily Partitioned Data

The challenge in privacy-preserving data mining is avoiding the invasion of personal data privacy. Secure computa- tion provides a solution to this problem. With the development of this technique, fully homomorphic encryption has been realized after decades of research; this encryption enables the c...

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Vydáno v:International journal of advanced computer science & applications Ročník 4; číslo 9
Hlavní autoři: Li, Yu, Zhong, Sheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: West Yorkshire Science and Information (SAI) Organization Limited 01.01.2013
Témata:
Algorithms
Clustering
Cryptography
Data mining
Encryption
Privacy
ISSN:2158-107X, 2156-5570
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Shrnutí:The challenge in privacy-preserving data mining is avoiding the invasion of personal data privacy. Secure computa- tion provides a solution to this problem. With the development of this technique, fully homomorphic encryption has been realized after decades of research; this encryption enables the computing and obtaining results via encrypted data without accessing any plaintext or private key information. In this paper, we propose a privacy-preserving clustering using representatives (CURE) algorithm over arbitrarily partitioned data using fully homomor- phic encryption. Our privacy-preserving CURE algorithm allows cooperative computation without revealing users’ individual data. The method used in our algorithm enables the data to be arbitrarily distributed among different parties and to receive accurate clustering result simultaneously.
Bibliografie:ObjectType-Article-1
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2013.040932