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Oded Goldreich (2006)
Foundations of Cryptography: Volume 1
H. Subramaniam, R. Wright, Zhiqiang Yang (2004)
Experimental Analysis of Privacy-Preserving Statistics Computation
I. Tatarinov, Stratis Viglas, K. Beyer, J. Shanmugasundaram, E. Shekita, Chun Zhang (2002)
Storing and querying ordered XML using a relational database system
Wenliang Du, Y. Han, Shigang Chen (2004)
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification
Sheng Zhong, Zhiqiang Yang, R. Wright (2005)
Privacy-enhancing k-anonymization of customer data
J. Kleinberg, C. Papadimitriou, P. Raghavan (2000)
Auditing Boolean attributes
Zhiqiang Yang, Sheng Zhong, R. Wright (2005)
Anonymity-preserving data collection
Jaideep Vaidya, Chris Clifton (2004)
Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data
Wenliang Du, J. Zhan (2003)
Using randomized response techniques for privacy-preserving data mining
Oded Goldreich, S. Micali, A. Wigderson (1987)
How to play ANY mental gameProceedings of the nineteenth annual ACM symposium on Theory of computing
(1996)
Applied Cryptography, 2nd edn
(1996)
Matrix Computation, 3rd edn
Oded Goldreich (2004)
Foundations of Cryptography
Bart Goethals, S. Laur, H. Lipmaa, Taneli Mielikäinen (2004)
On Private Scalar Product Computation for Privacy-Preserving Data Mining
Zhiqiang Yang, R. Wright (2005)
Improved Privacy-Preserving Bayesian Network Parameter Learning on Vertically Partitioned Data21st International Conference on Data Engineering Workshops (ICDEW'05)
A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrke (2002)
Privacy preserving mining of association rulesProceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
S. Rizvi, J. Haritsa (2002)
Maintaining Data Privacy in Association Rule Mining
R. Agrawal, R. Srikant (2000)
Privacy-preserving data mining
Zhiqiang Yang, Sheng Zhong, R. Wright (2005)
Privacy-Preserving Classification of Customer Data without Loss of Accuracy
Jaideep Vaidya, Chris Clifton (2003)
Privacy-preserving k-means clustering over vertically partitioned data
Da Meng, K. Sivakumar, H. Kargupta (2004)
Privacy-sensitive Bayesian network parameter learningFourth IEEE International Conference on Data Mining (ICDM'04)
S. Stolfo, A. Prodromidis, Shelley Tselepis, Wenke Lee, David Fan, P. Chan (1997)
JAM: Java Agents for Meta-Learning over Distributed Databases
Nan Zhang, Shengquan Wang, Wei Zhao (2005)
A new scheme on privacy-preserving data classification
Zhengli Huang, Wenliang Du, Biao Chen (2005)
Deriving private information from randomized data
Goldreich Oded (2004)
Foundations of Cryptography: Volume 2, Basic Applications
M. Freedman, Kobbi Nissim, Benny Pinkas (2004)
Efficient Private Matching and Set Intersection
Si-yang Gu (2006)
Privacy preserving association rule mining in vertically partitioned dataJournal of Computer Applications
Murat Kantarcioglu, Chris Clifton (2004)
Privacy-preserving distributed mining of association rules on horizontally partitioned dataIEEE Transactions on Knowledge and Data Engineering, 16
R. Canetti, Y. Ishai, Ravi Kumar, M. Reiter, R. Rubinfeld, R. Wright (2001)
Selective private function evaluation with applications to private statistics
D. Agrawal, C. Aggarwal (2001)
On the design and quantification of privacy preserving data mining algorithms
Jaideep Vaidya, Chris Clifton (2005)
Secure set intersection cardinality with application to association rule miningJ. Comput. Secur., 13
R. Agrawal, A. Evfimievski, R. Srikant (2003)
Information sharing across private databases
(2003)
Multiplicative noise, random projection, and privacy preserving data mining from distributed multiparty data
G. Jagannathan, R. Wright (2005)
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
H. Kargupta, Souptik Datta, Qi Wang, K. Sivakumar (2003)
On the privacy preserving properties of random data perturbation techniquesThird IEEE International Conference on Data Mining
M. Atallah, Wenliang Du (2001)
Secure Multi-party Computational Geometry
A. Evfimievski, J. Gehrke, R. Srikant (2003)
Limiting privacy breaches in privacy preserving data mining
F. Chin (1986)
Security problems on inference control for SUM, MAX, and MIN queriesJ. ACM, 33
R. Wright, Zhiqiang Yang (2004)
Privacy-preserving Bayesian network structure computation on distributed heterogeneous dataProceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Catherine Blake (1998)
UCI Repository of machine learning databases
There have been two methods for privacy- preserving data mining: the perturbation approach and the cryptographic approach. The perturbation approach is typically very efficient, but it suffers from a tradeoff between accuracy and privacy. In contrast, the cryptographic approach usually maintains accuracy, but it is more expensive in computation and communication overhead. We propose a novel perturbation method, called guided perturbation . Specifically, we focus on a central problem of privacy-preserving data mining—the secure scalar product problem of vertically partitioned data, and give a solution based on guided perturbation, with good, provable privacy guarantee. Our solution achieves accuracy comparable to the cryptographic solutions, while keeping the efficiency of perturbation solutions. Our experimental results show that it can be more than one hundred times faster than a typical cryptographic solution.
The VLDB Journal – Springer Journals
Published: Aug 1, 2008
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