.png){kind=icon}
.png){kind=icon}
Return
Privacy-Enhanced Offline Data-Driven Evolutionary Optimization Based on Cloud Server
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2025.3584882.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
data-driven evolutionary algorithms (DDEAs) have achieved significant success in numerous real-world optimization problems, where exact objective functions and constraint functions do not exist, and they mainly rely on available data. However, the existing DDEAs primarily focus on improving performance through data and surrogate, without considering that the users may lack the specialized domain knowledge and sufficient computing resources required for DDEAs. To address the aforementioned issues, this article proposes a novel paradigm called evolutionary learning and optimization as a service (ELOaaS) and investigates the potential collusion attacks between machine learningmachine learning modules and evolutionary computing modules on cloud server, which may lead to privacy leakage. Consequently, a privacy-enhanced (privacy-enhanced offline DDEA (PEDDEA)) is proposed as an instantiation algorithm of ELOaaS, which is designed to tackle offline data-driven evolutionary optimization within the ELOaaS paradigm. In the proposed PEDDEA, a subspace learning-based privacy protection strategy is designed to defense the collusion attacks. Additionally, a model management strategy based on Kendall tau metric is introduced to construct high-quality surrogate ensembles. PEDDEA enables users to outsource private offline data to cloud servers, thereby approaching the optimal solution while ensuring privacy protection. Comprehensive experiments are conducted on benchmark problems and safety evaluation problems of autonomous vehiclesautonomous vehicle. According to the experimental results, the proposed algorithm has significant performance advantages over existing offline DDEAs while ensuring privacy protection.
Keywords:
Data-driven optimization
evolutionary computation (EC)
evolutionary learning and optimization as a service (ELOaaS)
model management
privacy protection
Journal
IF:
12
Papers:
1.8K
Citations:
2.4W