通信学报2018，Vol.39Issue(1)：70-77,8.DOI:10.11959/j.issn.1000-436x.2018013
MapReduce框架下支持差分隐私保护的随机梯度下降算法
Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework
摘要
Abstract
Aiming at the contradiction between the efficiency and privacy of stochastic gradient descent algorithm in distributed computing environment,a stochastic gradient descent algorithm preserving differential privacy based on MapReduce was proposed.Based on the computing framework of MapReduce,the data were allocated randomly to each Map node and the Map tasks were started independently to execute the stochastic gradient descent algorithm.The Reduce tasks were appointed to update the model when the sub-target update models were meeting the update requirements,and to add Laplace random noise to achieve differential privacy protection.Based on the combinatorial features of differential privacy,the results of the algorithm is proved to be able to fulfill ε-differentially private.The experimental results show that the algorithm has obvious efficiency advantage and good data availability.关键词
机器学习/随机梯度下降/MapReduce/差分隐私保护/拉普拉斯机制Key words
machine learning/stochastic gradient descent/MapReduce/differential privacy preserving/Laplace mechanism分类
信息技术与安全科学引用本文复制引用
俞艺涵,付钰,吴晓平..MapReduce框架下支持差分隐私保护的随机梯度下降算法[J].通信学报,2018,39(1):70-77,8.基金项目
国家自然科学基金资助项目(No.61100042) （No.61100042）
国家社科基金资助项目(No.15GJ003-201)The National Natural Science Foundation of China (No.61100042),The National Social Science Foundation of China (No.15GJ003-201) （No.15GJ003-201）
