# When Evolutionary Computation Meets Privacy

arXiv: https://arxiv.org/abs/2304.01205 · HTML: https://mlsanity.com/paper/2304.01205/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning](https://mlsanity.com/paper/2206.04621/) (2022)
- [The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)](https://mlsanity.com/paper/2011.02352/) (2020)
- [A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm](https://mlsanity.com/paper/2210.08295/) (2022)
- [Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization](https://mlsanity.com/paper/2205.13948/) (2022)

## Abstract (arXiv)

Recently, evolutionary computation (EC) has been promoted by machine learning, distributed computing, and big data technologies, resulting in new research directions of EC like distributed EC and surrogate-assisted EC. These advances have significantly improved the performance and the application scope of EC, but also trigger privacy leakages, such as the leakage of optimal results and surrogate model. Accordingly, evolutionary computation combined with privacy protection is becoming an emerging topic. However, privacy concerns in evolutionary computation lack a systematic exploration, especially for the object, motivation, position, and method of privacy protection. To this end, in this paper, we discuss three typical optimization paradigms (i.e., textitcentralized optimization, distributed optimization, and data-driven optimization) to characterize optimization modes of evolutionary computation and propose BOOM to sort out privacy concerns in evolutionary computation. Specifically, the centralized optimization paradigm allows clients to outsource optimization problems to the centralized server and obtain optimization solutions from the server. While the distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. Also, the data-driven optimization paradigm utilizes data collected in history to tackle optimization problems lacking explicit objective functions. Particularly, this paper adopts BOOM to characterize the object and motivation of privacy protection in three typical optimization paradigms and discusses potential privacy-preserving technologies balancing optimization performance and privacy guarantees in three typical optimization paradigms. Furthermore, this paper attempts to foresee some new research directions of privacy-preserving evolutionary computation.