When Evolutionary Computation Meets Privacy

doi:10.1109/MCI.2023.3327892

When Evolutionary Computation Meets Privacy

2024-02-01

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Bowen Zhao

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陈伟能 (Wei–Neng Chen) *

DOI：10.1109/MCI.2023.3327892

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Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns regarding privacy leakages, specifically the disclosure of optimal results and surrogate models. Consequently, the combination of evolutionary computation and privacy protection becomes an increasing necessity. However, a comprehensive exploration of privacy concerns in evolutionary computation is currently lacking, particularly in terms of identifying the object, motivation, position, and method of privacy protection. To address this gap, this paper aims to discuss three typical optimization paradigms, namely, centralized optimization, distributed optimization, and data-driven optimization, to characterize optimization modes of evolutionary computation and proposes BOOM (i.e., oBject, mOtivation, pOsition, and Method) to sort out privacy concerns related to evolutionary computation. In particular, the centralized optimization paradigm allows clients to outsource optimization problems to a centralized server and obtain optimization solutions from the server. The distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. On the other hand, the data-driven optimization paradigm utilizes historical data to address optimization problems without explicit objective functions. Within each of these paradigms, BOOM is used to characterize the object and motivation of privacy protection. Furthermore, this paper discuss the potential privacy-preserving technologies that strike a balance between optimization performance and privacy guarantees. Finally, this paper outlines several new research directions for privacy-preserving evolutionary computation.

Keywords:

Privacy

Data privacy

Evolutionary computation

Machine learning

Linear programming

Servers

Object recognition

Centralized optimization

data-driven optimization

distributed optimization

evolutionary computation

privacy protection

When Evolutionary Computation Meets Privacy

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