document: Privacy-preservation for gradient descent methods 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 multi-party 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 .
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