Survey of K-anonymity research on privacy preservation
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K-anonymity is a highlighted topic of privacy preservation research in recent years.In this paper,the concepts of K-anonymity and K-Minimal anonymity are described.Then,generalization suppression,K-anonymity evaluation criterion,and many different algorithms proposed previously are presented.Finally,the future directions in this field are discussed.Keywords:
k-Anonymity
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k-anonymity is a well-known definition of privacy, which guarantees that any person in the released dataset cannot be distinguished from at least k-1 other individuals. In the protection model, the records are anonymized through generalization or suppression with a fixed value of k. Accordingly, each record has the same level of anonymity in the published dataset. However, different people or items usually have inconsistent privacy requirements. Some records need extra protection while others require a relatively low level of privacy constraint. In this paper, we propose Multi-Level Privacy Preserving K-Anonymity, an advanced protection model based on k-anonymity, which divides records into different groups and requires each group to satisfy its respective privacy requirement. Moreover, we present a practical algorithm using clustering techniques to ensure the property. The evaluation on a real-world dataset confirms that the proposed method has the advantages of offering more flexibility in setting privacy parameters and providing higher data utility than traditional k-anonymity.
k-Anonymity
Data anonymization
Privacy Protection
Privacy software
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k-Anonymity
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Journal Article Proposed Anonymity for Authors Get access Robert Wyatt Robert Wyatt Department of Botany, Duke University, Durham, NC 27706 Search for other works by this author on: Oxford Academic Google Scholar BioScience, Volume 27, Issue 1, January 1977, Page 5, https://doi.org/10.2307/1297783 Published: 01 January 1977
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Many applications employing the data mining techniques involve mining the data that includes private and sensitive information about the subjects. K-anonymity is a property that models the protection of released data against possible re-identification of the respondents to which the data refers. One of the interesting aspects of k-anonymity is its association with protection techniques that preserve the truthfulness of the data. It is however evident that the collection and analysis of data that include personal information may violate the privacy of the individuals to whom information refers. To guarantee the k-anonymity requirement, k-anonymity requires each quasi-identifier value in the released table to have at least k occurrences. In this paper, we present a survey of recent approaches that have been applied to the k-Anonymity problem.
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Data anonymization
Identification
Table (database)
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Data publishing
k-Anonymity
Data anonymization
Data Sharing
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Privacy is a concept directly related to people's interest in maintaining personal space without the interference of others. In this paper, we focus on study the k-anonymity technique since many generalization algorithms are based on this privacy model. Due to this, we develop a proof of concept that uses the k-anonymity technique for data anonymization to anonymize data raw and generate a new file with anonymized data. We present the system architecture and detailed an experiment using the adult data set which has sensitive information, where each record corresponds to the personal information for a person. Finally, we summarize our work and discuss future works.
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Information sensitivity
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Privacy preservation has been an essential issue for individuals or organizations.k-anonymity is one of the primary techniques realizing privacy protection in data dissemination environment.Current k-anonymity solutions based on generalization and suppression techniques suffer from high information loss and low usability mainly due to reliance on pre-defined generalization hierarchies or order imposed on each attribute domain.It develops a new k-anonymity algorithm based on clustering technology.Experimental results show that the method can improve the usability of the released data while preserving privacy.
k-Anonymity
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Privacy Protection
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