Privacy-Preserving Clustering Using Representatives over Arbitrarily Partitioned Data
S. Yu Li. International Journal of Advanced Computer Science and Applications(IJACSA), (2013)
Zusammenfassung
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.
%0 Journal Article
%1 IJACSA.2013.040932
%A Yu Li, Sheng Zhong
%D 2013
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K imported
%N 9
%T Privacy-Preserving Clustering Using Representatives over Arbitrarily Partitioned Data
%U http://ijacsa.thesai.org/
%V 4
%X 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.
@article{IJACSA.2013.040932,
abstract = {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.},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{Yu Li}, Sheng Zhong},
biburl = {https://www.bibsonomy.org/bibtex/2f4699d56323da8e3a6bb9b4f3241fefb/thesaiorg},
interhash = {0d694921b90f5acaa6f002ba3e05bab2},
intrahash = {f4699d56323da8e3a6bb9b4f3241fefb},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {imported},
number = 9,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{Privacy-Preserving Clustering Using Representatives over Arbitrarily Partitioned Data}},
url = {http://ijacsa.thesai.org/},
volume = 4,
year = 2013
}