Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework
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...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2018-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018013/ |
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| _version_ | 1841539459619749888 |
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| author | Yihan YU Yu FU Xiaoping WU |
| author_facet | Yihan YU Yu FU Xiaoping WU |
| author_sort | Yihan YU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cba7fe6084f24a90a6c27026748588c5 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-cba7fe6084f24a90a6c27026748588c52025-01-14T07:14:06ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2018-01-0139707759715939Stochastic gradient descent algorithm preserving differential privacy in MapReduce frameworkYihan YUYu FUXiaoping WUAiming 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018013/machine learningstochastic gradient descentMapReducedifferential privacy preservingLaplace mechanism |
| spellingShingle | Yihan YU Yu FU Xiaoping WU Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework Tongxin xuebao machine learning stochastic gradient descent MapReduce differential privacy preserving Laplace mechanism |
| title | Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework |
| title_full | Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework |
| title_fullStr | Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework |
| title_full_unstemmed | Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework |
| title_short | Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework |
| title_sort | stochastic gradient descent algorithm preserving differential privacy in mapreduce framework |
| topic | machine learning stochastic gradient descent MapReduce differential privacy preserving Laplace mechanism |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018013/ |
| work_keys_str_mv | AT yihanyu stochasticgradientdescentalgorithmpreservingdifferentialprivacyinmapreduceframework AT yufu stochasticgradientdescentalgorithmpreservingdifferentialprivacyinmapreduceframework AT xiaopingwu stochasticgradientdescentalgorithmpreservingdifferentialprivacyinmapreduceframework |