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» Data mining with differential privacy
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KDD
2004
ACM
160views Data Mining» more  KDD 2004»
16 years 4 days 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 9 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 5 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»
15 years 1 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»
16 years 4 days 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