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» Data mining with differential privacy
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KDD
2004
ACM
160views Data Mining» more  KDD 2004»
15 years 10 months ago
k-TTP: a new privacy model for large-scale distributed environments
Secure multiparty computation allows parties to jointly compute a function of their private inputs without revealing anything but the output. Theoretical results [2] provide a gen...
Bobi Gilburd, Assaf Schuster, Ran Wolff
SODA
2010
ACM
171views Algorithms» more  SODA 2010»
14 years 7 months ago
Differential Privacy in New Settings
Differential privacy is a recent notion of privacy tailored to the problem of statistical disclosure control: how to release statistical information about a set of people without ...
Cynthia Dwork
ECAI
2004
Springer
15 years 3 months ago
Inference Attacks in Peer-to-Peer Homogeneous Distributed Data Mining
Spontaneous formation of peer-to-peer agent-based data mining systems seems a plausible scenario in years to come. However, the emergence of peer-to-peer environments further exace...
Josenildo Costa da Silva, Matthias Klusch, Stefano...
ICEB
2004
175views Business» more  ICEB 2004»
14 years 11 months ago
Privacy-Preserving Data Mining in Electronic Surveys
Electronic surveys are an important resource in data mining. However, how to protect respondents' data privacy during the survey is a challenge to the security and privacy co...
Justin Z. Zhan, Stan Matwin
KDD
2004
ACM
137views Data Mining» more  KDD 2004»
15 years 10 months ago
When do data mining results violate privacy?
Privacy-preserving data mining has concentrated on obtaining valid results when the input data is private. An extreme example is Secure Multiparty Computation-based methods, where...
Murat Kantarcioglu, Jiashun Jin, Chris Clifton