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» Privacy Skyline: Privacy with Multidimensional Adversarial K...
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WWW
2009
ACM
15 years 10 months ago
To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles
In order to address privacy concerns, many social media websites allow users to hide their personal profiles from the public. In this work, we show how an adversary can exploit an...
Elena Zheleva, Lise Getoor
SIGMOD
2010
ACM
274views Database» more  SIGMOD 2010»
15 years 2 months ago
K-isomorphism: privacy preserving network publication against structural attacks
Serious concerns on privacy protection in social networks have been raised in recent years; however, research in this area is still in its infancy. The problem is challenging due ...
James Cheng, Ada Wai-Chee Fu, Jia Liu
CCS
2010
ACM
15 years 28 days ago
Towards publishing recommendation data with predictive anonymization
Recommender systems are used to predict user preferences for products or services. In order to seek better prediction techniques, data owners of recommender systems such as Netfli...
Chih-Cheng Chang, Brian Thompson, Hui (Wendy) Wang...
ICDE
2008
IEEE
498views Database» more  ICDE 2008»
16 years 9 months ago
Injector: Mining Background Knowledge for Data Anonymization
Existing work on privacy-preserving data publishing cannot satisfactorily prevent an adversary with background knowledge from learning important sensitive information. The main cha...
Tiancheng Li, Ninghui Li
ICFP
2010
ACM
14 years 10 months ago
Distance makes the types grow stronger: a calculus for differential privacy
We want assurances that sensitive information will not be disclosed when aggregate data derived from a database is published. Differential privacy offers a strong statistical guar...
Jason Reed, Benjamin C. Pierce