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» Deriving Private Information from Randomized Data
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SDM
2008
SIAM
177views Data Mining» more  SDM 2008»
14 years 11 months ago
Practical Private Computation and Zero-Knowledge Tools for Privacy-Preserving Distributed Data Mining
In this paper we explore private computation built on vector addition and its applications in privacypreserving data mining. Vector addition is a surprisingly general tool for imp...
Yitao Duan, John F. Canny
CISC
2008
Springer
152views Cryptology» more  CISC 2008»
14 years 11 months ago
Privacy for Private Key in Signatures
One of the important applications of digital signature is anonymous credential or pseudonym system. In these scenarios, it is essential that the identity of the signer is kept secr...
Qianhong Wu, Bo Qin, Yi Mu, Willy Susilo
71
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TCC
2010
Springer
208views Cryptology» more  TCC 2010»
15 years 6 months ago
Rationality in the Full-Information Model
We study rationality in protocol design for the full-information model, a model characterized by computationally unbounded adversaries, no private communication, and no simultanei...
Ronen Gradwohl
PVLDB
2010
95views more  PVLDB 2010»
14 years 8 months ago
Small Domain Randomization: Same Privacy, More Utility
Random perturbation is a promising technique for privacy preserving data mining. It retains an original sensitive value with a certain probability and replaces it with a random va...
Rhonda Chaytor, Ke Wang
CRYPTO
2000
Springer
157views Cryptology» more  CRYPTO 2000»
15 years 2 months ago
Reducing the Servers Computation in Private Information Retrieval: PIR with Preprocessing
Private information retrieval (PIR) enables a user to retrieve a data item from a database, replicated among one or more servers, while hiding the identity of the retrieved item. ...
Amos Beimel, Yuval Ishai, Tal Malkin