Genetic algorithm-based clustering approach for k-anonymization

Lin, Jun-Lin; Wei, Meng-Cheng

doi:10.1016/j.eswa.2009.02.009

Cited by 28 publications

(7 citation statements)

References 26 publications

(30 reference statements)

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“…The groups are encoded as an alphabet with a fixed size as the maximal number of different groups can be derived from k and the number of records in the dataset. Lin et al [ 32 ] described how the scalability of the clustering process can be improved for large datasets by encoding the solution using the entire population instead of a single individual. Finally, focusing on data transformations, Iyengar [ 47 ] has demonstrated how a genetic algorithm can be used to determine intervals for generalizing values.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, it also suggests why it is often not feasible to search the entire solution space of all potential output datasets when processing more complex data. For this kind of task, solutions that try to determine a good transformation scheme on a best-effort basis, e.g., based on heuristic strategies [ 15 , 30 , 31 ] or clustering algorithms [ 16 , 17 , 32 ], have been developed. An overview of common types of approaches is provided by Fung et al [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

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A scalable software solution for anonymizing high-dimensional biomedical data

Meurers

Bild

et al. 2021

GigaScience

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Background Data anonymization is an important building block for ensuring privacy and fosters the reuse of data. However, transforming the data in a way that preserves the privacy of subjects while maintaining a high degree of data quality is challenging and particularly difficult when processing complex datasets that contain a high number of attributes. In this article we present how we extended the open source software ARX to improve its support for high-dimensional, biomedical datasets. Findings For improving ARX's capability to find optimal transformations when processing high-dimensional data, we implement 2 novel search algorithms. The first is a greedy top-down approach and is oriented on a formally implemented bottom-up search. The second is based on a genetic algorithm. We evaluated the algorithms with different datasets, transformation methods, and privacy models. The novel algorithms mostly outperformed the previously implemented bottom-up search. In addition, we extended the GUI to provide a high degree of usability and performance when working with high-dimensional datasets. Conclusion With our additions we have significantly enhanced ARX's ability to handle high-dimensional data in terms of processing performance as well as usability and thus can further facilitate data sharing.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A scalable software solution for anonymizing high-dimensional biomedical data

Meurers

Bild

et al. 2021

GigaScience

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“…Bewong et al 35 proposed the clustering method to achieve

{k}^m

‐anonymous and l ‐diverse privacy guarantees on transactional data. Lin et al 36 use the GA to cluster the dataset to achieve k ‐anonymization in the case of relational data. Recent work has proposed a k ‐anonymity First t ‐closeness aware algorithm (kafta) 34 that builds clusters while satisfying k ‐anonymity and t ‐closeness simultaneously using a micro aggregation algorithm.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The size of a cluster C is at least k , that is, each chromosome is at least of length k . The encoding is inspired by the encoding scheme proposed in Reference 36 for relational data.…”

Section: Publication Mechanismmentioning

confidence: 99%

(k, m, t)‐anonymity: Enhanced privacy for transactional data

Puri

Kaur

Sachdeva

2022

Concurrency and Computation

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Recent years have witnessed the wide availability of an array of transactional datasets for mining and other research activities. A primary concern related to the public sharing of transactional datasets is identifying individuals whose data is being published. Data anonymization is a commonly utilized privacy preservation method for preventing user identification. However, the existing anonymization models such as km$$ {k}^m $$‐anonymity, ρ$$ \rho $$‐uncertainty, and (h, k, p)‐coherence for privacy preservation of transactional data do not provide complete protection from the various types of possible privacy attacks. Therefore, this article proposes a novel privacy model called (k, m, t)‐anonymity to effectively prevent identity and attribute disclosure as well as skewness attack on transactional data. A genetic algorithm‐based implementation of the model is also presented. The genetic algorithm clusters transactional data based on the similarity among the transactions for effective km$$ {k}^m $$‐anonymization with low information loss. The clustering algorithm simultaneously aims to minimize the skewness of data distribution in the obtained clusters for preventing skewness attack on anonymized data. Experimental results have verified that the (k, m, t)‐anonymity model ensures transactional data anonymization without significant information loss. The proposed privacy model is implemented using the proposed approach on two real‐world datasets (health domain and click‐stream data) and an enormous dataset generated synthetically (health domain consisting of 5,00,000 records). The relative error is less as compared to the relative privacy and disassociation technique for all test case scenarios. Hence, the proposed anonymization model maintains the data utility.

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“…Anonymizing genetic data without destroying a large share of the information is not a simple task. Some methods, for instance, try to balance anonymity and information preservation by clustering the data before analysis (Lin and Wei 2009). Even when the data are labeled as anonymized, however, the inherent information they contain could allow for potential reidentification attacks (Wjst 2010).…”

Section: Identifiability and Informed Consentmentioning

confidence: 99%

Genetic Data: Potential Uses and Misuses in Marketing

Daviet¹,

Nave²,

Wind³

2021

Journal of Marketing

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Advances in molecular genetics have led to the exponential growth of the direct-to-consumer genetic testing industry, resulting in the assembly of massive privately-owned genetic databases. This article explores the potential impact of this new data type on the field of marketing. Drawing on findings from behavioral genetic research, we propose a framework that incorporates genetic influences into existing consumer behavior theory, and use it to survey potential marketing uses of genetic data. Applications include business strategies that rely on genetic variants as bases for segmentation and targeting, creative uses that develop consumers’ sense of community and personalization, use of genetically informed study designs to test causal relations, and refinement of consumer theory by uncovering biological mechanisms underlying behavior. We further evaluate ethical challenges related to autonomy, privacy, misinformation and discrimination that are unique to the use of genetic data and are not sufficiently addressed by current regulations. We conclude by proposing an agenda for future research.

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Genetic algorithm-based clustering approach for k-anonymization

Cited by 28 publications

References 26 publications

A scalable software solution for anonymizing high-dimensional biomedical data

A scalable software solution for anonymizing high-dimensional biomedical data

(k, m, t)‐anonymity: Enhanced privacy for transactional data

Genetic Data: Potential Uses and Misuses in Marketing

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