Privacy-Preserving Gradient-Descent Methods

• Published in: IEEE transactions on knowledge and data engineering Vol. 22; no. 6; pp. 884 - 899
• Main Authors: Shuguo Han, Wee Keong Ng, Li Wan, Lee, Vincent C S
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2010; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer Society; Computer systems and distributed systems. User interface; Data mining; Data privacy; Data processing. List processing. Character string processing; Descent; Exact sciences and technology; gradient-descent method; least square approach; Least squares method; Least squares methods; Linear regression; Machine learning; Machine learning algorithms; Mathematical models; Memory and file management (including protection and security); Memory organisation. Data processing; Optimization; Privacy; Privacy-preserving data mining; Protocols; secure multiparty computation; Sliding mode control; Software; stochastic approach; Stochastic processes; Data privacy; Data analysis; Probabilistic approach; Scalability; Decision making; Local optimum; secure multiparty computation; Data partition; Data mining; least square approach; Multiparty protocol; Gradient descent; gradient-descent method; Least squares method; Descent method; Problem solving; Privacy-preserving data mining; Artificial intelligence; Gradient method; stochastic approach; Mathematical programming
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2009.153

Gradient descent is a widely used paradigm for solving many optimization problems. Gradient descent aims 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. In this paper, we propose a preliminary formulation of gradient descent with data privacy preservation. We present two approaches-stochastic approach and least square approach-under different assumptions. Four protocols are proposed for the two approaches incorporating various secure building blocks for both horizontally and vertically partitioned data. We conduct experiments to evaluate the scalability of the proposed secure building blocks and the accuracy and efficiency of the protocols for four different scenarios. The excremental results show that the proposed secure building blocks are reasonably scalable and the proposed protocols allow us to determine a better secure protocol for the applications for each scenario.