A decision framework for privacy-preserving synthetic data generation

IF 4.9 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Pablo Sanchez-Serrano, Ruben Rios, Isaac Agudo
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引用次数: 0

Abstract

Access to realistic data is essential for various purposes, including training machine learning models, conducting simulations, and supporting data-driven decision making across diverse domains. However, the use of real data often raises significant privacy concerns, as it may contain sensitive or personal information. Generative models have emerged as a promising solution to this problem by generating synthetic datasets that closely resemble real data. Nevertheless, these models are typically trained on original datasets, which carries the risk of leaking sensitive information. To mitigate this issue, privacy-preserving generative models have been developed to balance data utility and privacy guarantees. This paper examines existing generative models for synthetic tabular data generation, proposing a taxonomy of solutions based on the privacy guarantees they provide. Additionally, we present a decision framework to aid in selecting the most suitable privacy-preserving generative model for specific scenarios, using privacy and utility metrics as key selection criteria.

Abstract Image

一种保护隐私的合成数据生成决策框架
获取真实数据对于各种目的都是必不可少的,包括训练机器学习模型、进行模拟和支持跨不同领域的数据驱动决策。然而,使用真实数据往往会引起严重的隐私问题,因为它可能包含敏感或个人信息。生成模型通过生成与真实数据非常相似的合成数据集,成为解决这一问题的一种很有前途的方法。然而,这些模型通常是在原始数据集上训练的,这有泄露敏感信息的风险。为了缓解这一问题,已经开发了保护隐私的生成模型来平衡数据效用和隐私保证。本文研究了合成表格数据生成的现有生成模型,提出了一种基于它们提供的隐私保证的解决方案分类。此外,我们提出了一个决策框架,以帮助为特定场景选择最合适的隐私保护生成模型,使用隐私和效用指标作为关键选择标准。
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来源期刊
Computers & Electrical Engineering
Computers & Electrical Engineering 工程技术-工程:电子与电气
CiteScore
9.20
自引率
7.00%
发文量
661
审稿时长
47 days
期刊介绍: The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.
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