| 题名 | On the Privacy Issue of Evolutionary Biparty Multiobjective Optimization |
| 作者 | |
| 通讯作者 | Luo，Wenjian |
| DOI | |
| 发表日期 | 2023
|
| ISSN | 0302-9743
|
| EISSN | 1611-3349
|
| 会议录名称 | |
| 卷号 | 13968 LNCS
|
| 页码 | 371-382
|
| 摘要 | Some evolutionary algorithms have been proposed to address biparty multiobjective optimization problems (BPMOPs). However, all these algorithms are centralized algorithms which directly obtain the privacy information including objective functions from decision makers (DMs). This paper transforms the centralized algorithm OptMPNDS2 into a distributed framework for BPMOPs and focuses on the privacy issue in the framework. The framework has a server and two clients, and each client belongs to a DM. The clients keep their objective functions locally, evaluate individuals, and upload Pareto levels and crowding distances of all individuals to the server. The server performs the other operations including reproduction and selection of offspring. Experimental results show that the performance of the framework is very close to OptMPNDS2. Besides, two privacy attacks are proposed when one client is malicious. Experimental results show that the client could steal approximate Pareto optimal solutions of the other honest DM. |
| 关键词 | |
| 学校署名 | 其他
|
| 语种 | 英语
|
| 相关链接 | [Scopus记录] |
| 收录类别 | |
| EI入藏号 | 20233514656655
|
| EI主题词 | Cell proliferation
; Decision making
; Evolutionary algorithms
; Pareto principle
|
| EI分类号 | Biology:461.9
; Management:912.2
; Optimization Techniques:921.5
|
| Scopus记录号 | 2-s2.0-85169013661
|
| 来源库 | Scopus
|
| 引用统计 |
被引频次[WOS]：0
|
| 成果类型 | 会议论文 |
| 条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/560106 |
| 专题 | 工学院_计算机科学与工程系 |
| 作者单位 | 1.Guangdong Provincial Key Laboratory of Novel Intelligence Technologies,School of Computer Science and Technology,Harbin Institute of Technology,Shenzhen,Guangdong,518055,China 2.Peng Cheng Laboratory,Shenzhen,Guangdong,518055,China 3.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China |
| 推荐引用方式 GB/T 7714 |
She，Zeneng,Luo，Wenjian,Chang，Yatong,et al. On the Privacy Issue of Evolutionary Biparty Multiobjective Optimization[C],2023:371-382.
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| 条目包含的文件 | 条目无相关文件。 | |||||
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