Limiting sensitive values in an anonymized table while reducing information loss via <i>p</i>‐proportion

Dosselmann, Richard; Hamilton, Howard J.

doi:10.1002/spy2.202

Cited by 2 publications

(3 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, Aminifar et al's approach undertakes a binary classification of sensitive attributes, neglecting to secure attributes classified as negative. In addition, although AM, 42 a technology grounded in the Mondrian algorithm, represents a recent advancement, it was also eschewed from this study. As discussed in the relevant research section, anatomy possesses superior accuracy compared to Mondrian; therefore, we opted for a comparison with HBA, which is predicated upon the anatomy algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…Herein, we discuss the most recent advancement in anonymization technology, adapted Mondrian (AM), introduced by Dosselmann et al 42 AM is a refined version of the earlier anonymization algorithm Mondrian 22 . Dosselmann and associates categorize sensitive attributes into “sensitive values” and “nonsensitive values” to minimize information loss.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Algorithm to satisfy l‐diversity by combining dummy records and grouping

Oishi,

Sei,

Andrew

et al. 2024

Security and Privacy

View full text Add to dashboard Cite

Universities and corporations frequently use personal information databases for diverse objectives, such as research and marketing. The use of these databases inherently intersects with privacy issues, which have been the subject of extensive research. Traditional anonymization techniques predominantly focus on removing or altering identifiers and quasi‐identifiers (QIDs), the latter of which, although not unique, are closely correlated with individuals. However, this modification of QIDs can often impede data analysis. In this study, we introduce an innovative anonymization algorithm that combines the dummy‐record addition technique with a grouping method while circumventing the modification of QIDs. This fusion reduces the number of dummy records required for effective anonymization. The principal contribution of this study is the algorithm's ability to reduce the number of added dummy records. The proposed algorithm not only retains a high degree of data usefulness but also successfully adheres to the ‐diversity standard, which is a critical metric in privacy security. The experimental findings demonstrate that the proposed method offers a more equitable balance between safety and utility than existing technological solutions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Algorithm to satisfy l‐diversity by combining dummy records and grouping

Oishi,

Sei,

Andrew

et al. 2024

Security and Privacy

View full text Add to dashboard Cite

show abstract

“…The algorithm requires that the frequency of sensitive data in the same equivalent class with high sensitivity is less than the threshold, but it cannot guarantee the security of sensitive data with low sensitivity. Onesimu et al [ 21 ] and Dosselmann et al [ 22 ] generate equivalence classes through a bottom-up clustering algorithm and an improved Mondrian algorithm, respectively. Then, they generalize each equivalence class to achieve data anonymity.…”

Section: Related Workmentioning

confidence: 99%

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks

Huang

Miao

et al. 2023

Sensors

View full text Add to dashboard Cite

Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution’s difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks.

show abstract

Limiting sensitive values in an anonymized table while reducing information loss via p‐proportion

Cited by 2 publications

References 51 publications

Algorithm to satisfy l‐diversity by combining dummy records and grouping

Algorithm to satisfy l‐diversity by combining dummy records and grouping

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks

Contact Info

Product

Resources

About