A New Evolutionary Computation Framework for Privacy-Preserving Optimization

Zhan, Zhi-Hui; Wu, Sheng-Hao; Zhang, Mengjie

doi:10.1109/icaci52617.2021.9435860

Cited by 14 publications

(7 citation statements)

References 39 publications

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“…This particular situation gives rise to a complex and significant challenge referred to as privacy-preserving optimization. In this context, the traditional assumption of having access to the objective function no longer holds true, necessitating the development of novel approaches and techniques to address this issue [29]. Addressing privacy-preserving optimization has emerged as a new and challenging frontier in the evolutionary computation community.…”

Section: Proposed Methodology Regarding the First Research Objectivementioning

confidence: 99%

Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization in Audio-Video Content

Parnianifard

2024

Preprint

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This research study focuses on addressing practical optimization challenges in audio-visual content using advanced computational intelligence methods, with a specific emphasis on privacy-preserving issues. The research will be presented as a proposal and can be conducted based on the provided ideas. Computational intelligence encompasses various approaches, including evolutionary algorithms, learning methods, fuzzy systems, and neural networks, which have become essential tools for tackling complex engineering optimization problems due to computational expenses, dimensional complexities, and interdisciplinary integration. The research objectives include a thorough investigation of optimization challenges in audio-visual content, the delineation of problem characteristics, and the development of relevant methodologies based on these specifications. The main goals of the study, titled "Advanced Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization," encompass the development of a metamodel-based learning algorithm integrated with evolutionary computation for privacy-preserving optimization, less-expensive signal watermarking optimization using hybrid metamodeling and evolutionary algorithms, and a closed-loop robust intelligent PID control system applied in real-time dynamic signal watermarking. Additional objectives may be defined as the research progresses and further background review is conducted.

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Section: Proposed Methodology Regarding the First Research Objectivementioning

confidence: 99%

Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization in Audio-Video Content

Parnianifard

2024

Preprint

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“…For the particle p n i , i D 1; 2; : : : ; s i , during the specific particle updating process, SA-MDP first calculates the modifying distance as Eq. (18).…”

Section: Evolutionmentioning

confidence: 99%

“…Compared with the general EA [18] , SA-MDP contains an extra operation: Mother particle splitting. Based on the introduced concept, the old particle can split into several new particles, it improves the diversity of the solution and the exploration ability of SA-MDP.…”

Section: Evaluation Of the Efficiencymentioning

confidence: 99%

An Optimized Sanitization Approach for Minable Data Publication

Yang

Liao

2022

Big Data Min. Anal.

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Minable data publication is ubiquitous since it is beneficial to sharing/trading data among commercial companies and further facilitates the development of data-driven tasks. Unfortunately, the minable data publication is often implemented by publishers with limited privacy concerns such that the published dataset is minable by malicious entities. It prohibits minable data publication since the published data may contain sensitive information. Thus, it is urgently demanded to present some approaches and technologies for reducing the privacy leakage risks.To this end, in this paper, we propose an optimized sanitization approach for minable data publication (named as SA-MDP). SA-MDP supports association rules mining function while providing privacy protection for specific rules.In SA-MDP, we consider the trade-off between the data utility and the data privacy in the minable data publication problem. To address this problem, SA-MDP designs a customized particle swarm optimization (PSO) algorithm, where the optimization objective is determined by both the data utility and the data privacy. Specifically, we take advantage of PSO to produce new particles, which is achieved by random mutation or learning from the best particle.Hence, SA-MDP can avoid the solutions being trapped into local optima. Besides, we design a proper fitness function to guide the particles to run towards the optimal solution. Additionally, we present a preprocessing method before the evolution process of the customized PSO algorithm to improve the convergence rate. Finally, the proposed SA-MDP approach is performed and verified over several datasets. The experimental results have demonstrated the effectiveness and efficiency of SA-MDP.

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“…However, the work [19] fails to support privacypreserving selection operations, and no practical problem is involved to evaluate its effectiveness and efficiency. Zhan et al [20] proposed a rank-based cryptographic function (RCF) to construct privacy-preserving EAs including particle swarm optimization and differential evolution. However, the authors do not the construct of RCF and their scheme suffers from some privacy concerns.…”

Section: Related Workmentioning

confidence: 99%

“…However, the authors do not the construct of RCF and their scheme suffers from some privacy concerns. Although a designer in [20] fails to obtain the fitness function, he holds possible solutions. Thus, as long as the designer learns which solution is dominant, he can obtain the approximate optimal solution, which discloses a user's privacy.…”

Section: Related Workmentioning

confidence: 99%

Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization

Chen¹,

Wei²,

Liu³

et al. 2022

Preprint

Self Cite

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Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users do not have enough capability to implement EAs to solve COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, whilst it suffers from privacy concerns. To this end, this paper proposes a novel computing paradigm, evolution as a service (EaaS), where a cloud server renders evolutionary computation services for users without sacrificing users' privacy. Inspired by the idea of EaaS, this paper designs PEGA, a novel privacy-preserving GA for COPs. Specifically, PEGA enables users outsourcing COPs to the cloud server holding a competitive GA and approximating the optimal solution in a privacy-preserving manner. PEGA features the following characteristics. First, any user without expertise and enough resources can solve her COPs. Second, PEGA does not leak contents of optimization problems, i.e., users' privacy. Third, PEGA has the same capability as the conventional GA to approximate the optimal solution. We implements PEGA falling in a twin-server architecture and evaluates it in the traveling salesman problem (TSP, a widely known COP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suit of secure computing protocols to support evolutionary operators of GA on encrypted data. Privacy analysis demonstrates that PEGA does not disclose the contents of the COP to the cloud server. Experimental evaluation results on four TSP datasets show that PEGA is as effective as the conventional GA in approximating the optimal solution.

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A New Evolutionary Computation Framework for Privacy-Preserving Optimization

Cited by 14 publications

References 39 publications

Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization in Audio-Video Content

Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization in Audio-Video Content

An Optimized Sanitization Approach for Minable Data Publication

Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization

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