Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Robustness of Linear Discriminant Analysis in Automatic Speech Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Privacy-preservation for gradient descent methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Handwritten digit recognition with nonlinear fisher discriminant analysis
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Privacy-preserving genetic algorithms for rule discovery
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Privacy-preserving self-organizing map
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
A Privacy Preserving Markov Model for Sequence Classification
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Privacy-preserving Kruskal-Wallis test
Computer Methods and Programs in Biomedicine
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Privacy-preserving data mining enables two or more parties to collaboratively perform data mining while preserving the data privacy of the participating parties. So far, various data mining and machine learning algorithms have been enhanced to incorporate privacy preservation. In this paper, we propose privacy-preserving solutions for Fisher Discriminant Analysis (FDA) over horizontally and vertically partitioned data. FDA is one of the widely used discriminant algorithms that seeks to separate different classes as much as possible for discriminant analysis or dimension reduction. It has been applied to face recognition, speech recognition, and handwriting recognition. The secure solutions are designed based on two basic secure building blocks that we have proposed--the Secure Matrix Multiplication protocol and the Secure Inverse of Matrix Sum protocol--which are in turn based on cryptographic techniques. We conducted experiments to evaluate the scalability of the proposed secure building blocks and overheads to achieve privacy when performing FDA.