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Privacy-preservation for gradient descent methods (2007)
| Venue: | In SIGKDD |
| Citations: | 8 - 1 self |
BibTeX
@INPROCEEDINGS{Wan07privacy-preservationfor,
author = {Li Wan and Shuguo Han and Vincent C. S. Lee},
title = {Privacy-preservation for gradient descent methods},
booktitle = {In SIGKDD},
year = {2007},
pages = {775--783}
}
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Abstract
Gradient descent is a widely used paradigm for solving many optimization problems. Stochastic gradient descent performs a series of iterations to minimize a target function in order to reach a local minimum. In machine learning or data mining, this function corresponds to a decision model that is to be discovered. The gradient descent paradigm underlies many commonly used techniques in data mining and machine learning, such as neural networks, Bayesian networks, genetic algorithms, and simulated annealing. To the best of our knowledge, there has not been any work that extends the notion of privacy preservation or secure multiparty computation to gradient-descent-based techniques. In this paper, we propose a preliminary approach to enable privacy preservation in gradient descent methods in general and demonstrate its feasibility in specific gradient descent methods.
Keyphrases
gradient descent method machine learning privacy preservation data mining secure multiparty computation target function genetic algorithm stochastic gradient descent bayesian network local minimum specific gradient descent method many optimization problem gradient descent preliminary approach gradient descent paradigm underlies decision model neural network gradient-descent-based technique
