LI Kunlun
,
WANG Zhe
,
ZHANG Juan
,
WU Qian
,
SONG Song
. SNS privacy protection based on the ELM integration and semi-supervised clustering[J]. Journal of Hebei University(Natural Science Edition), 2013
, 33(1)
: 84
-89
.
DOI: 10.3969/j.issn.1000-1565.2013.01.016
[1] 李昆仑, 张超, 曹铮, 刘明, LI Kun-lun, ZHANG Chao, CAO Zheng, LIU Ming. 基于Seed集的半监督核聚类 [J]. 计算机工程与应用 2009.doi:10.3778/j.issn.1002-8331.2009.20.046
[2] SAMARATI P, SWEENEY L. Generalizing data to provide anonymity when disclosing information [A]. New York:ACM 1998.
[3] AGGARWAL G, FEDER T, KENTHAPADI K. Achieving anonymity via clustering [A]. New York:ACM 2006.
[4] MEYERSON A, WILLIAMS R. On the complexity of optimal k-anonymity [A]. New York:ACM 2004.
[5] IYENGAR V. Transforming data to satisfy privacy constraints [A]. New York:ACM 2002.
[6] LEFEVRE K, DEWITT D, RAMAKRISHNAN R. Incognito:Efficient full-domain k-anonymity [A]. New York:ACM 2005.
[7] BAYARDO R, AGRAWAL R. Data privacy through optimal K-anonymization [A]. Los Alamitos 2005.
[8] HE Wei, LIU Xing. A cluster-based protocol to enforce integrity and preserve privacy in data aggregation [A]. Montreal, Canada 2009.
[9] LIN Jing, WEN Tong. Density-based microaggregation for statistical disclosure control [J]. ACM Trans Algorithms 2010, 6(03).
[10] LEFEVRE K, DEWITT D, RAMAKRISHNAN R. Mondrian multidimensional k-anonymity [A]. Los Alamitos 2006.
[11] SOLANAS A, MARTINEZ BALLESTE A, DOMINGO FERRER J. A 2d-tree-based blocking method for microaggreagting very large data sets [A]. Reliability and Security Sydney 2006.
[12] DOMING-FERRER J, TORRA V. Ordinal, continuous and heteroge-neous k-anonymitythrough microaggreagtion [J]. Data Mining and Knowledge Discovery 2005, 11(02).
[13] CHENG Jing, LIU Jia. K-isomorphism:Privacy preserving network publication against structural attacks [R]. Proceedings of the ACM SIGMOD International Conference on Management of Data, New York 2010.
[14] 李昆仑, 曹铮, 刘明. 半监督聚类的若干新发展 [J]. 模式识别与人工智能 2010, 22(05).
[15] MACQUEEN J. Some methods for classification and analysis of multivariate observations [A]. Berkeley, California, USA 1967.
[16] HUANG Guangbin. Extreme learning machine:A new learning scheme of feedforward neural networks [J]. Neurocomputing 2006, 70(08).