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» Quantifying and Measuring Anonymity
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FSKD
2008
Springer
136views Fuzzy Logic» more  FSKD 2008»
14 years 18 hour ago
k-Anonymity via Clustering Domain Knowledge for Privacy Preservation
Preservation of privacy in micro-data release is a challenging task in data mining. The k-anonymity method has attracted much attention of researchers. Quasiidentifier is a key co...
Taiyong Li, Changjie Tang, Jiang Wu, Qian Luo, She...
CCS
2010
ACM
13 years 11 months ago
Detecting and characterizing social spam campaigns
Online social networks (OSNs) are popular collaboration and communication tools for millions of users and their friends. Unfortunately, in the wrong hands, they are also effective...
Hongyu Gao, Jun Hu, Christo Wilson, Zhichun Li, Ya...
SIGMOD
2006
ACM
96views Database» more  SIGMOD 2006»
14 years 11 months ago
Injecting utility into anonymized datasets
Limiting disclosure in data publishing requires a careful balance between privacy and utility. Information about individuals must not be revealed, but a dataset should still be us...
Daniel Kifer, Johannes Gehrke
WWW
2007
ACM
14 years 11 months ago
Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography
In a social network, nodes correspond to people or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anon...
Lars Backstrom, Cynthia Dwork, Jon M. Kleinberg
PAKDD
1999
ACM
87views Data Mining» more  PAKDD 1999»
14 years 3 months ago
On Information-Theoretic Measures of Attribute Importance
Abstract. An attribute is deemed important in data mining if it partitions the database such that previously unknown regularities are observable. Many information-theoretic measure...
Yiyu Yao, S. K. Michael Wong, Cory J. Butz