Clustering algorithm preserving differential privacy in the framework of Spark
Aimed at the problem that traditional methods fail to deal with malicious attacks with arbitrary background knowledge during the process of massive data clustering analysis,an improved clustering algorithm， especially designed for preserving differential privacy,under the framework of Spark was prop...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2016-11-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2016.00087 |
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| _version_ | 1841530321457119232 |
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| author | Zhi-qiang GAO Qing-peng LI Ren-yuan HU |
| author_facet | Zhi-qiang GAO Qing-peng LI Ren-yuan HU |
| author_sort | Zhi-qiang GAO |
| collection | DOAJ |
| description | Aimed at the problem that traditional methods fail to deal with malicious attacks with arbitrary background knowledge during the process of massive data clustering analysis,an improved clustering algorithm， especially designed for preserving differential privacy,under the framework of Spark was proposed.Furthermore,it’s theoretically proved to meet the standard of ε-differential privacy in the framework of Spark platform.Finally,experimental results show that guaranteeing the availability of proposed clustering algorithm,the improved algorithm has an advantage over privacy protection and satisfaction in the aspect of time as well as efficiency.Most importantly,the proposed algorithm shows a good application prospect in the analysis of data clustering preserving privacy protection and data security. |
| format | Article |
| id | doaj-art-c74af12633a6411cbbe72ca8c08c66ef |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2016-11-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-c74af12633a6411cbbe72ca8c08c66ef2025-01-15T03:05:02ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2016-11-012475159549125Clustering algorithm preserving differential privacy in the framework of SparkZhi-qiang GAOQing-peng LIRen-yuan HUAimed at the problem that traditional methods fail to deal with malicious attacks with arbitrary background knowledge during the process of massive data clustering analysis,an improved clustering algorithm， especially designed for preserving differential privacy,under the framework of Spark was proposed.Furthermore,it’s theoretically proved to meet the standard of ε-differential privacy in the framework of Spark platform.Finally,experimental results show that guaranteeing the availability of proposed clustering algorithm,the improved algorithm has an advantage over privacy protection and satisfaction in the aspect of time as well as efficiency.Most importantly,the proposed algorithm shows a good application prospect in the analysis of data clustering preserving privacy protection and data security.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2016.00087Spark,differential privacyclustering algorithmdata miningbig data analysis |
| spellingShingle | Zhi-qiang GAO Qing-peng LI Ren-yuan HU Clustering algorithm preserving differential privacy in the framework of Spark 网络与信息安全学报 Spark,differential privacy clustering algorithm data mining big data analysis |
| title | Clustering algorithm preserving differential privacy in the framework of Spark |
| title_full | Clustering algorithm preserving differential privacy in the framework of Spark |
| title_fullStr | Clustering algorithm preserving differential privacy in the framework of Spark |
| title_full_unstemmed | Clustering algorithm preserving differential privacy in the framework of Spark |
| title_short | Clustering algorithm preserving differential privacy in the framework of Spark |
| title_sort | clustering algorithm preserving differential privacy in the framework of spark |
| topic | Spark,differential privacy clustering algorithm data mining big data analysis |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2016.00087 |
| work_keys_str_mv | AT zhiqianggao clusteringalgorithmpreservingdifferentialprivacyintheframeworkofspark AT qingpengli clusteringalgorithmpreservingdifferentialprivacyintheframeworkofspark AT renyuanhu clusteringalgorithmpreservingdifferentialprivacyintheframeworkofspark |