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SIGMOD
2011
ACM
179views Database» more  SIGMOD 2011»
12 years 8 months ago
No free lunch in data privacy
Differential privacy is a powerful tool for providing privacypreserving noisy query answers over statistical databases. It guarantees that the distribution of noisy query answers...
Daniel Kifer, Ashwin Machanavajjhala
PVLDB
2008
101views more  PVLDB 2008»
13 years 5 months ago
Output perturbation with query relaxation
Given a dataset containing sensitive personal information, a statistical database answers aggregate queries in a manner that preserves individual privacy. We consider the problem ...
Xiaokui Xiao, Yufei Tao
PODS
2010
ACM
306views Database» more  PODS 2010»
13 years 11 months ago
Optimizing linear counting queries under differential privacy
Differential privacy is a robust privacy standard that has been successfully applied to a range of data analysis tasks. But despite much recent work, optimal strategies for answe...
Chao Li, Michael Hay, Vibhor Rastogi, Gerome Mikla...
STOC
2009
ACM
145views Algorithms» more  STOC 2009»
14 years 6 months ago
Differential privacy and robust statistics
We show by means of several examples that robust statistical estimators present an excellent starting point for differentially private estimators. Our algorithms use a new paradig...
Cynthia Dwork, Jing Lei
STOC
2007
ACM
112views Algorithms» more  STOC 2007»
14 years 6 months ago
Smooth sensitivity and sampling in private data analysis
We introduce a new, generic framework for private data analysis. The goal of private data analysis is to release aggregate information about a data set while protecting the privac...
Kobbi Nissim, Sofya Raskhodnikova, Adam Smith