Privacy preserving protocols for eigenvector computation
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Efficient Protocols for Principal Eigenvector Computation over Private Data
Transactions on Data Privacy
Privacy-preserving back-propagation and extreme learning machine algorithms
Data & Knowledge Engineering
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
A near-optimal algorithm for differentially-private principal components
The Journal of Machine Learning Research
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In this paper, we propose secure protocols to perform Singular Value Decomposition (SVD) for two parties over horizontally and vertically partitioned data. We propose various secure building blocks for the computations of QR algorithm so that it is privacy-preserving. Some of the proposed secure building blocks include Secure Matrix Multiplication, $(x+y)^{-1}$, and $\sqrt{x+y}$. Together, they allow us to derive Privacy-Preserving SVD (PPSVD) based on a privacy-preserving QR algorithm. Finally we conduct experiments to evaluate the proposed secure building blocks and protocols. The results show that the proposed protocols for SVD achieve high accuracy for matrices of small and medium size.