佩加:一种保护隐私的遗传算法,用于组合优化
IEEE transactions on cybernetics
|January 12, 2024
概括
本研究介绍了基于云计算的进化计算作为服务 (ECaaS) 的隐私保护遗传算法 (PEGA). PEGA使用户能够安全有效地解决复杂的组合优化问题,而不会泄露敏感数据.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 密码学 密码学 密码学 密码学
背景情况:
- 对于缺乏专业知识或资源的用户来说,组合优化问题 (COP) 是一个挑战.
- 将这些计算外包到云端引发了严重的隐私问题.
- 现有的解决方案往往需要专业知识或损害数据保密性.
研究的目的:
- 提出一种新的计算范式,即进化计算作为服务 (ECaaS).
- 为COPs引入一个保护隐私的遗传算法 (PEGA).
- 为那些资源或专业知识有限的用户提供安全有效的基于云的优化.
主要方法:
- 为PEGA开发了一个双服务器架构.
- 在加密数据上利用加密密码学和安全计算协议为GA运营商.
- 实施和评估PEGA在旅行销售员问题 (TSP) 和0/1Knapsack问题 (KP) 上.
主要成果:
- PEGA有效地使用户能够在没有领域专业知识或足够的资源的情况下解决COP.
- 该系统通过防止优化问题细节的泄露,成功地保护了用户的隐私.
- 在接近最佳解决方案方面,PEGA表现出与传统遗传算法相当的性能.
结论:
- 佩加为保护隐私的基于云的优化提供了一个可行的解决方案.
- 在ECaaS范式促进访问先进的计算能力,同时确保数据保密.
- 实验结果验证了PEGA对TSP和KP的有效性和安全性.
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