When evolutionary computation meets privacy
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 adva...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2024
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/8651 https://ink.library.smu.edu.sg/context/sis_research/article/9654/viewcontent/2304.01205.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-9654 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-96542024-02-22T03:08:37Z When evolutionary computation meets privacy ZHAO, Bowen CHEN, Wei-Neng LI, Xiaoguo LIU, Ximeng PEI, Qingqi ZHANG, Jun 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. 2024-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8651 info:doi/10.1109/MCI.2023.3327892 https://ink.library.smu.edu.sg/context/sis_research/article/9654/viewcontent/2304.01205.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Centralized optimization data-driven optimization distributed optimization evolutionary computation privacy protection Databases and Information Systems Information Security |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Centralized optimization data-driven optimization distributed optimization evolutionary computation privacy protection Databases and Information Systems Information Security |
spellingShingle |
Centralized optimization data-driven optimization distributed optimization evolutionary computation privacy protection Databases and Information Systems Information Security ZHAO, Bowen CHEN, Wei-Neng LI, Xiaoguo LIU, Ximeng PEI, Qingqi ZHANG, Jun When evolutionary computation meets privacy |
description |
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. |
format |
text |
author |
ZHAO, Bowen CHEN, Wei-Neng LI, Xiaoguo LIU, Ximeng PEI, Qingqi ZHANG, Jun |
author_facet |
ZHAO, Bowen CHEN, Wei-Neng LI, Xiaoguo LIU, Ximeng PEI, Qingqi ZHANG, Jun |
author_sort |
ZHAO, Bowen |
title |
When evolutionary computation meets privacy |
title_short |
When evolutionary computation meets privacy |
title_full |
When evolutionary computation meets privacy |
title_fullStr |
When evolutionary computation meets privacy |
title_full_unstemmed |
When evolutionary computation meets privacy |
title_sort |
when evolutionary computation meets privacy |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2024 |
url |
https://ink.library.smu.edu.sg/sis_research/8651 https://ink.library.smu.edu.sg/context/sis_research/article/9654/viewcontent/2304.01205.pdf |
_version_ |
1794549705071394816 |