Matrix analysis
The nature of statistical learning theory
The nature of statistical learning theory
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A new scheme on privacy-preserving data classification
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
IEEE Transactions on Knowledge and Data Engineering
Privacy Preserving Data Classification with Rotation Perturbation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Privacy Preserving ID3 Algorithm over Horizontally Partitioned Data
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data
Proceedings of the 2006 ACM symposium on Applied computing
Cryptographically private support vector machines
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving decision tree in multi party environment
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
A privacy-preserving classification mining algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Reduced Support Vector Machines: A Statistical Theory
IEEE Transactions on Neural Networks
Computational Optimization and Applications
Privacy-preserving outsourcing support vector machines with random transformation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Differentially Private Empirical Risk Minimization
The Journal of Machine Learning Research
Cloud-enabled privacy-preserving collaborative learning for mobile sensing
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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We propose a novel privacy-preserving support vector machine (SVM) classifier for a data matrix A whose input feature columns are divided into groups belonging to different entities. Each entity is unwilling to share its group of columns or make it public. Our classifier is based on the concept of a reduced kernel K(A, B′), where B′ is the transpose of a random matrix B. The column blocks of B corresponding to the different entities are privately generated by each entity and never made public. The proposed linear or nonlinear SVM classifier, which is public but does not reveal any of the privately held data, has accuracy comparable to that of an ordinary SVM classifier that uses the entire set of input features directly.