Efficient privacy-preserving decision tree classification protocol
To provide privacy-preserving decision tree classification services in the Internet of things (IoT) big data scenario, an efficient privacy-preserving decision tree classification protocol was proposed by adopting the secure multiparty computation framework into the classification model.The entire p...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2021-08-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021149/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539303322157056 |
|---|---|
| author | Lichuan MA Jiayi PENG Qingqi PEI Haojin ZHU |
| author_facet | Lichuan MA Jiayi PENG Qingqi PEI Haojin ZHU |
| author_sort | Lichuan MA |
| collection | DOAJ |
| description | To provide privacy-preserving decision tree classification services in the Internet of things (IoT) big data scenario, an efficient privacy-preserving decision tree classification protocol was proposed by adopting the secure multiparty computation framework into the classification model.The entire protocol consisted of three parts: the original decision tree model mixing, the Boolean share-based privacy-preserving comparing, and the 1-out-of-n oblivious transfer-based classification result obtaining.Via the proposed protocol, the service providers could protect the parameters of their decision tree models and the users were able to derive the classification result without exposing their privately hold data.Through a concrete security analysis, the proposed protocol was proved to be secure against semi-honest adversaries.By implementing the proposed protocol on various practical decision tree models from open datasets, the classification accuracy and the average time cost for completing one privacy-preserving classification service were evaluated.After compared with existing related works, the performance superiority of the proposed protocol is demonstrated. |
| format | Article |
| id | doaj-art-b5c82da20cbb4ee8b472d1dcb9399f67 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-b5c82da20cbb4ee8b472d1dcb9399f672025-01-14T07:22:19ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-08-0142808959743294Efficient privacy-preserving decision tree classification protocolLichuan MAJiayi PENGQingqi PEIHaojin ZHUTo provide privacy-preserving decision tree classification services in the Internet of things (IoT) big data scenario, an efficient privacy-preserving decision tree classification protocol was proposed by adopting the secure multiparty computation framework into the classification model.The entire protocol consisted of three parts: the original decision tree model mixing, the Boolean share-based privacy-preserving comparing, and the 1-out-of-n oblivious transfer-based classification result obtaining.Via the proposed protocol, the service providers could protect the parameters of their decision tree models and the users were able to derive the classification result without exposing their privately hold data.Through a concrete security analysis, the proposed protocol was proved to be secure against semi-honest adversaries.By implementing the proposed protocol on various practical decision tree models from open datasets, the classification accuracy and the average time cost for completing one privacy-preserving classification service were evaluated.After compared with existing related works, the performance superiority of the proposed protocol is demonstrated.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021149/decision treeprivacy preservingoblivious transfersecure multiparty computation |
| spellingShingle | Lichuan MA Jiayi PENG Qingqi PEI Haojin ZHU Efficient privacy-preserving decision tree classification protocol Tongxin xuebao decision tree privacy preserving oblivious transfer secure multiparty computation |
| title | Efficient privacy-preserving decision tree classification protocol |
| title_full | Efficient privacy-preserving decision tree classification protocol |
| title_fullStr | Efficient privacy-preserving decision tree classification protocol |
| title_full_unstemmed | Efficient privacy-preserving decision tree classification protocol |
| title_short | Efficient privacy-preserving decision tree classification protocol |
| title_sort | efficient privacy preserving decision tree classification protocol |
| topic | decision tree privacy preserving oblivious transfer secure multiparty computation |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021149/ |
| work_keys_str_mv | AT lichuanma efficientprivacypreservingdecisiontreeclassificationprotocol AT jiayipeng efficientprivacypreservingdecisiontreeclassificationprotocol AT qingqipei efficientprivacypreservingdecisiontreeclassificationprotocol AT haojinzhu efficientprivacypreservingdecisiontreeclassificationprotocol |