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PODS
2011
ACM
163views Database» more  PODS 2011»
14 years 2 months ago
Provenance views for module privacy
Scientific workflow systems increasingly store provenance information about the module executions used to produce a data item, as well as the parameter settings and intermediate...
Susan B. Davidson, Sanjeev Khanna, Tova Milo, Debm...
SIGMOD
2011
ACM
179views Database» more  SIGMOD 2011»
14 years 2 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
ICDE
2009
IEEE
192views Database» more  ICDE 2009»
16 years 1 months ago
Deriving Private Information from Association Rule Mining Results
Data publishing can provide enormous benefits to the society. However, due to privacy concerns, data cannot be published in their original forms. Two types of data publishing can a...
Zutao Zhu, Guan Wang, Wenliang Du
PODS
2006
ACM
121views Database» more  PODS 2006»
15 years 12 months ago
On the efficiency of checking perfect privacy
Privacy-preserving query-answering systems answer queries while provably guaranteeing that sensitive information is kept secret. One very attractive notion of privacy is perfect p...
Ashwin Machanavajjhala, Johannes Gehrke
EDBT
2008
ACM
154views Database» more  EDBT 2008»
15 years 12 months ago
Data utility and privacy protection trade-off in k-anonymisation
K-anonymisation is an approach to protecting privacy contained within a dataset. A good k-anonymisation algorithm should anonymise a dataset in such a way that private information...
Grigorios Loukides, Jianhua Shao