{"title":"流形中的隐私:将 K 匿名与弗雷谢特手段上的差分隐私相结合","authors":"","doi":"10.1016/j.cose.2024.103983","DOIUrl":null,"url":null,"abstract":"<div><p>While anonymization techniques have improved greatly in allowing data to be used again, it is still really hard to get useful information from anonymized data without risking people’s privacy. Conventional approaches such as k-Anonymity and Differential Privacy have limitations in preserving data utility and privacy simultaneously, particularly in high-dimensional spaces with manifold structures. We address this challenge by focusing on anonymizing data existing within high-dimensional spaces possessing manifold structures. To tackle these issues, we propose and implement a hybrid anonymization scheme termed as the (<span><math><mi>β</mi></math></span>, <span><math><mi>k</mi></math></span>, <span><math><mi>b</mi></math></span>)-anonymization method that combines elements of both differential privacy and k-anonymity. This approach aims to produce high-quality anonymized data that closely resembles real data in terms of knowledge extraction while safeguarding privacy. The Fréchet mean, an operation applicable in metric spaces and meaningful in the manifold setting, serves as a key aspect of our approach. It provides insight into the geometry of data points within high-dimensional spaces. Our goal is to anonymize this Fréchet mean using our proposed approach and minimize the distance between the original and anonymized Fréchet mean to achieve data privacy without significant loss of information. Additionally, we introduce a novel Fréchet mean clustering model designed to enhance the clustering process for high-dimensional spaces. Through theoretical analysis and practical experiments, we demonstrate that our approach outperforms traditional privacy models both in terms of preserving data utility and privacy. This research contributes to advancing privacy-preserving techniques for complex and non-linear data structures, ensuring a balance between data utility and privacy protection.</p></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167404824002888/pdfft?md5=a14f469316460402540287437b369b27&pid=1-s2.0-S0167404824002888-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Privacy in manifolds: Combining k-anonymity with differential privacy on Fréchet means\",\"authors\":\"\",\"doi\":\"10.1016/j.cose.2024.103983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>While anonymization techniques have improved greatly in allowing data to be used again, it is still really hard to get useful information from anonymized data without risking people’s privacy. Conventional approaches such as k-Anonymity and Differential Privacy have limitations in preserving data utility and privacy simultaneously, particularly in high-dimensional spaces with manifold structures. We address this challenge by focusing on anonymizing data existing within high-dimensional spaces possessing manifold structures. To tackle these issues, we propose and implement a hybrid anonymization scheme termed as the (<span><math><mi>β</mi></math></span>, <span><math><mi>k</mi></math></span>, <span><math><mi>b</mi></math></span>)-anonymization method that combines elements of both differential privacy and k-anonymity. This approach aims to produce high-quality anonymized data that closely resembles real data in terms of knowledge extraction while safeguarding privacy. The Fréchet mean, an operation applicable in metric spaces and meaningful in the manifold setting, serves as a key aspect of our approach. It provides insight into the geometry of data points within high-dimensional spaces. Our goal is to anonymize this Fréchet mean using our proposed approach and minimize the distance between the original and anonymized Fréchet mean to achieve data privacy without significant loss of information. Additionally, we introduce a novel Fréchet mean clustering model designed to enhance the clustering process for high-dimensional spaces. Through theoretical analysis and practical experiments, we demonstrate that our approach outperforms traditional privacy models both in terms of preserving data utility and privacy. 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引用次数: 0
摘要
虽然匿名技术在允许数据再次使用方面有了很大改进,但要从匿名数据中获取有用信息而又不危及个人隐私,仍然非常困难。k 匿名和差分隐私等传统方法在同时保护数据效用和隐私方面存在局限性,尤其是在具有流形结构的高维空间中。我们将重点放在对具有流形结构的高维空间中存在的数据进行匿名处理,从而应对这一挑战。为了解决这些问题,我们提出并实施了一种混合匿名方案,称为 (β, k, b)匿名方法,它结合了差分隐私和 k 匿名的元素。这种方法旨在生成高质量的匿名数据,在知识提取方面与真实数据非常相似,同时保护隐私。弗雷谢特均值是一种适用于度量空间的运算,在流形设置中意义重大,是我们方法的一个关键方面。它能让我们深入了解高维空间中数据点的几何形状。我们的目标是使用我们提出的方法对弗雷谢特均值进行匿名化,并最小化原始弗雷谢特均值与匿名化弗雷谢特均值之间的距离,从而在不丢失大量信息的情况下实现数据隐私。此外,我们还引入了一种新的弗雷谢特均值聚类模型,旨在增强高维空间的聚类过程。通过理论分析和实际实验,我们证明了我们的方法在保护数据效用和隐私方面都优于传统的隐私模型。这项研究有助于推进复杂和非线性数据结构的隐私保护技术,确保数据实用性和隐私保护之间的平衡。
Privacy in manifolds: Combining k-anonymity with differential privacy on Fréchet means
While anonymization techniques have improved greatly in allowing data to be used again, it is still really hard to get useful information from anonymized data without risking people’s privacy. Conventional approaches such as k-Anonymity and Differential Privacy have limitations in preserving data utility and privacy simultaneously, particularly in high-dimensional spaces with manifold structures. We address this challenge by focusing on anonymizing data existing within high-dimensional spaces possessing manifold structures. To tackle these issues, we propose and implement a hybrid anonymization scheme termed as the (, , )-anonymization method that combines elements of both differential privacy and k-anonymity. This approach aims to produce high-quality anonymized data that closely resembles real data in terms of knowledge extraction while safeguarding privacy. The Fréchet mean, an operation applicable in metric spaces and meaningful in the manifold setting, serves as a key aspect of our approach. It provides insight into the geometry of data points within high-dimensional spaces. Our goal is to anonymize this Fréchet mean using our proposed approach and minimize the distance between the original and anonymized Fréchet mean to achieve data privacy without significant loss of information. Additionally, we introduce a novel Fréchet mean clustering model designed to enhance the clustering process for high-dimensional spaces. Through theoretical analysis and practical experiments, we demonstrate that our approach outperforms traditional privacy models both in terms of preserving data utility and privacy. This research contributes to advancing privacy-preserving techniques for complex and non-linear data structures, ensuring a balance between data utility and privacy protection.
期刊介绍:
Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world.
Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.