Privacy Violation Issues in Re-publication of Modification Datasets

Riyana, Noppamas; Riyana, Surapon; Nanthachumphu, Srikul; Sittisung, Suphannika; Duangban, Dussadee

doi:10.1007/978-3-030-68154-8_79

Cited by 5 publications

(4 citation statements)

References 13 publications

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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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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.

Self Cite

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“…For this reason, a challenge of data utilization is when datasets are utilized in big data analytics or shared with the outside scope of data collecting organizations, it is how to balance data utility and data privacy because they are traded-off. To achieve these aims in datasets, there are several well-known privacy preservation models to be proposed such as data anonymization models [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44], data anatomization models [45] [46] [47] [48], and aggregate query frameworks [49]. Moreover, we further see that some privacy preservation models have been proposed, they are based on data anonymization in conjunction with aggregate query frameworks such as k-Likeness [50] and (l p1 , .…”

Section: Introductionmentioning

confidence: 99%

Aggregate Query Frameworks for Preserving Privacy Data in Big Data Analytics

Riyana

Sasujit

Homdoung

2023

Preprint

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Data utility and data privacy are both serious issues that must be considered when datasets are released to use in big data analytics because they are traded-off issues. That is, high-utility datasets are generally high risks in terms of privacy violation issues. Likewise, datasets are formed to be high security in terms of privacy preservation, they often lead to data utility issues. To address these issues in datasets, several privacy preservation models have been proposed such as k-Anonymity, l-Diversity, t-Closeness, Anatomy, k-Likeness, and (lp1, . . . , lpn)-Privacy, i.e., all users’ explicit identifier values and all unique quasi-identifier values in datasets are removed and distorted respectively. Unfortunately, these privacy preservation models are static data models and still have data utility issues that must be addressed. Thus, they could be insufficient to address privacy violation issues in big data analytics. For this reason, a new efficient and effective privacy preservation model is proposed in this work such that it is based on aggregate query answers that can guarantee the confidence of the range and the minimized number of values that can be re-identified. The aims of the proposed privacy preservation model are that aside from privacy preservation constraints, the complexity and data utility are also maintained as much as possible. Furthermore, we show that the proposed model is a privacy preservation model that is efficient and effective by using extensive experiments.

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“…For this reason, balancing the data utility and the data privacy is a challenge in datasets when datasets are released and provided to the data analyst. To achieve data utility and data privacy, there are several well-known privacy preservation models to be proposed, i.e., data anonymization models [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44], data anatomization models [45] [46] [47] [48], and aggregate query frameworks [49]. In addition, some privacy preservation models are based on data anonymization in conjunction with aggregate query frameworks such as k-Likeness [50] and (l p1 , .…”

Section: Introductionmentioning

confidence: 99%

Privacy-Enhancing Data Aggregation for Big Data Analytics

Riyana,

Sasujit,

Homdoung

2023

ECTI-CIT Transactions

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Data utility and data privacy are serious issues that must be considered when datasets are utilized in big data analytics such that they are traded off. That is, the datasets have high data utility and often have high risks in terms of privacy violation issues. To balance the data utility and the data privacy in datasets when they are provided to utilize in big data analytics, several privacy preservation models have been proposed, e.g., k-Anonymity, l-Diversity, t-Closeness, Anatomy, k-Likeness, and (lp1, . . . , lpn)-Privacy. Unfortunately, these privacy preservation models are highly complex data models and still have data utility issues that must be addressed. To rid these vulnerabilities of these models, a new privacy preservation model is proposed in this work. It is based on aggregate query answers that can guarantee the confidence of the range and the number of values that can be re-identified. Furthermore, we show that the proposed model is more effcient and effective in big data analytics by using extensive experiments.

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Privacy Violation Issues in Re-publication of Modification Datasets

Cited by 5 publications

References 13 publications

Programming Techniques for Considering m Desired Conditions from n Possible Conditions

Programming Techniques for Considering m Desired Conditions from n Possible Conditions

Aggregate Query Frameworks for Preserving Privacy Data in Big Data Analytics

Privacy-Enhancing Data Aggregation for Big Data Analytics

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