摘要
针对现有分布式计算环境下随机梯度下降算法存在效率性与私密性矛盾的问题,提出一种MapReduce框架下满足差分隐私的随机梯度下降算法。该算法基于MapReduce框架,将数据随机分配到各个Map节点并启动Map分任务独立并行执行随机梯度下降算法;启动Reduce分任务合并满足更新要求的分目标更新模型,并加入拉普拉斯随机噪声实现差分隐私保护。根据差分隐私保护原理,证明了算法满足e-差分隐私保护要求。实验表明该算法具有明显的效率优势并有较好的数据可用性。
Aiming at the contradiction between the efficiency and privacy of stochastic gradient descent algorithm in dis-tributed computing environment, a stochastic gradient descent algorithm preserving differential privacy based on Ma-pReduce 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.
出处
《通信学报》
EI
CSCD
北大核心
2018年第1期70-77,共8页
Journal on Communications
基金
国家自然科学基金资助项目(No.61100042)
国家社科基金资助项目(No.15GJ003-201)~~
