Privacy Threats and Privacy Preservation Techniques for Farmer Data Collections Based on Data Shuffling

Riyana, Surapon; Ito, Nobuyasu; Chaiya, Tatsanee; Sriwichai, Uthaiwan; Dussadee, Natthawud; Chaichana, Thanapong; Assawarachan, Rittichai; Maneechukate, Thongchai; Tantikul, Samerkhwan; Riyana, Noppamas

doi:10.37936/ecti-cit.2022163.246469

Cited by 3 publications

(6 citation statements)

References 11 publications

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“…From the experimental results that are shown in Figures 5,6,7,8,9, and 10, we can see that the number of sensitive attributes and the value of l is more effective to the data utility in datasets than the number of quasi-identifier attributes because the privacy preservation constraint of the proposed model and the compared models is based on the number of the distinct sensitive values.…”

Section: • the Second Experimental Dataset Just Containsmentioning

confidence: 98%

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: • the Second Experimental Dataset Just Containsmentioning

confidence: 98%

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

Riyana

2023

Preprint

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“…To rid the vulnerabilities of data anonymization, data anatomization [45] [46] [47] [48] was proposed. For privacy preservation, all explicit identifiers of users are removed.…”

Section: Data Anatomization [45]mentioning

confidence: 99%

“…Observation 5 (Data anatomization) Although privacy preservation based on data anatomization is efficient and effective than data anonymization, we cannot deny that privacy preservation based on data anatomization still has some data utility issues that must be addressed. Moreover, privacy preservation based on data anatomization further has disorganized issues [48] that must be addressed when the number of sensitive attributes is increased.…”

Section: Data Anatomization [45]mentioning

confidence: 99%

“…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%

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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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“…Privacy violation is a serious issue that the data holder must consider when datasets are released to utilize in the outside scope of data-collecting organizations [1], i.e., the data holder must ensure that when the datasets are released, they must not have any concern of privacy violation issues. To achieve these aims in released datasets, k-anonymity is proposed.…”

Section: Introductionmentioning

confidence: 99%

Achieving Privacy Preservation Constraints based on K-Anonymity in conjunction with Adjacency Matrix and Weighted Graphs

Riyana,

Sasujit,

Homdoung

2024

ECTI-CIT Transactions

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A well-known privacy preservation model is k-anonymity. It is simple and widely applied in several real-life systems. To achieve k-anonymity constraints in datasets, all explicit identifiers of users are removed. Furthermore, the unique quasi-identifiers of users are distorted by their less specific values to be at least k indistinguishable tuples. For this reason, after datasets are satisfied by k-anonymity constraints, they can guarantee that all possible query conditions to them always have at least k tuples that are satisfied. Aside from achieving privacy preservation constraints, the data utility and the complexity of data transformation are serious issues that must also be considered when datasets are released. Therefore, both privacy preservation models are proposed in this work. They are based on k-anonymity constraints in conjunction with the weighted graph of correlated distortion tuples and the adjacency matrix of tuple distances. The proposed models aim to preserve data privacy in datasets. Moreover, the data utility and data transform complexities are also considered in the privacy preservation constraint of the proposed models. Furthermore, we show that the proposed data transformation technique is more efficient and effective by using extensive experiments.

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Privacy Threats and Privacy Preservation Techniques for Farmer Data Collections Based on Data Shuffling

Cited by 3 publications

References 11 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

Aggregate Query Frameworks for Preserving Privacy Data in Big Data Analytics

Achieving Privacy Preservation Constraints based on K-Anonymity in conjunction with Adjacency Matrix and Weighted Graphs

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