Enhanced (k,e)-anonymous for categorical data

Riyana, Surapon; Riyana, Noppamas; Nanthachumphu, Srikul

doi:10.1145/3056662.3056668

Cited by 17 publications

(8 citation statements)

References 14 publications

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“…Thus, the data utility metric is a necessity of the proposed privacy preservation model. Since privacy preservation based on data suppression and generalization has been presented, several data utility metrics have been proposed, e.g., Precision metric (P REC) for data suppression in conjunction with data generalization [36], Discernibility metric (DM ) [37], and Relative error [38] [39]. They will be explained in Sections 3.2.1, 3.2.2, and 3.2.3 respectively.…”

Section: Data Utility Metricmentioning

confidence: 99%

Privacy Preservation Models for the Independent Data Release of High-Dimensional Datasets

Riyana

2023

Preprint

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A major challenge is when datasets are released to utilize in the outside scope of data-collecting organizations, it is how to balance data utilities and data privacies. To achieve this aim in data collection (datasets), there are several privacy preservation models that have been proposed such as k-Anonymity and l-Diversity. Unfortunately, these privacy preservation models can be sufficient to address privacy violation issues in datasets that do not have high-dimensional attributes. For this reason, a privacy preservation model, LKC-Privacy, can address privacy violation issues in high-dimensional datasets to be proposed. With this privacy preservation model, datasets cannot have any concern of privacy violation issues when all L-size distinct quasi-identifier values are distorted (suppressed or generalized) to be at least K indistinguishable tuples. Moreover, every protected sensitive value relates to each group of indistinguishable quasi-identifier values, it must have the confidence of re-identifications to be at most C. Although LKC-Privacy is more efficient and effective than k-Anonymity and l-Diversity, it is generally efficient and effective to address privacy violation issues in location-based datasets. Moreover, we see that datasets satisfy LKC-Privacy constraints, they still have privacy violation issues from using data comparison attacks and they further have data utility issues that must be addressed. Therefore, a new privacy preservation model can address privacy violation issues in high-dimensional datasets such that its satiable released datasets do not have any concern of privacy violation issues from using data comparison attacks and are highly efficient and effective in data maintenance to be proposed in this work. Furthermore, we show that the proposed model is more efficient and effective by using extensive experiments.

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Section: Data Utility Metricmentioning

confidence: 99%

Privacy Preservation Models for the Independent Data Release of High-Dimensional Datasets

Riyana

2023

Preprint

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“…For this reason, software development and management techniques are in place to ensure efficient operations across the software. Aside from software development and management techniques, data security [27][28][29] , data privacy [30][31][32][33][34] , and data complexity [35,36] must also be considered. The complexity of software generally directs to affect the software performances [37][38][39] and the usage resources [40] .…”

Section: Related Workmentioning

confidence: 99%

Programming Techniques for Considering m Desired Conditions from n Possible Conditions

Riyana

Homdoung

Sasujit

2022

digit. tech. res. and appl.

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The performance of computer programs (or the hardware that can be programmed such as IoTs, embedded computers, and PLCs) is generally based on the complexity of the particular program development technique. High complexity often uses more execution times and system resources. For this reason, the computer program is less computational complexity to be desired. The conditional statements tell computers what certain information is a major cause of computer program complexities, e.g., considering m desired conditions from n possible conditions. To achieve this aim in computer programs, the data combination is often utilized. However, it is high complexity. Moreover, they cannot give that one condition takes precedence over others. To rid these vulnerabilities of combined conditions, a simple programming technique for considering m desired conditions from n possible conditions is proposed in this work, which is based on the summation of the condition weights. It only has the complexity of search spaces and data constructions to be O(n) and each condition can be set to be different precedence from the others. Furthermore, the proposed technique is evaluated by extensive experiments. From the experimental results, they indicate that the proposed technique is more effective and efficient than the comparative technique.

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“…In addition, each group of the indistinguishable quasi-identifier tuples in released datasets is denoted as an equivalence class. Aside from DGH, VGH, and suppression, there are several proposed approaches which can also be used to distort the quasi-identifier values in released datasets to the satisfaction of k-anonymity constraints such as swapped values [25,27,33], the range of values [5], and NDGH [23,26].…”

Section: Related Work K-anonymity [31]mentioning

confidence: 99%

“…To address privacy violation issues in released datasets from using linkage attacks, there are various anonymization models to be proposed such as [19,21,25,27,31,33]. With these anonymization models, aside from removing EI values, the re-identifiable QI values must also be eliminated by using such a suitable approach such as DGH [26,30,31], NDGH [23,26], swapped values [25,27], and the range of values [5]. However, these anonymization models are generally proposed to address privacy violation issues in datasets which are assumed that all attributes must be completed.…”

Section: Definition 2 (Linkage Attack)mentioning

confidence: 99%

Achieving Privacy Preservation Constraints in Missing-Value Datasets

2020

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Privacy violation issues must be taken into consideration when datasets are released for public use. To address these issues, there are various anonymization models to be proposed, e.g., k-anonymity, l-diversity, and t-closeness. However, these anonymization models generally propose to address privacy violation issues in datasets which are assumed that all attributes of them must be completed. Thus, these anonymization models could be insufficient to address privacy violation issues in such a dataset which is allowed to collect missing-values, e.g., rating datasets and trajectory datasets. Therefore, a new appropriate privacy preservation model for missing-value datasets is proposed by this work. With the proposed model, aside from privacy preservation, the data utility is also maintained as much as possible. Moreover, a suitable data utility metric for missing-value datasets is also presented by this work. Furthermore, the proposed model is shown that it is an NP-Complete problem by reduction from the X3C problem.

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Enhanced (k,e)-anonymous for categorical data

Cited by 17 publications

References 14 publications

Privacy Preservation Models for the Independent Data Release of High-Dimensional Datasets

Privacy Preservation Models for the Independent Data Release of High-Dimensional Datasets

Programming Techniques for Considering m Desired Conditions from n Possible Conditions

Achieving Privacy Preservation Constraints in Missing-Value Datasets

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