Efficient L-Diversity Algorithm for Preserving Privacy of Dynamically Published Datasets

Temuujin, Odsuren; Ahn, Jinhyun; Im, Dong-Hyuk

doi:10.1109/access.2019.2936301

Cited by 30 publications

(15 citation statements)

References 23 publications

(20 reference statements)

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“…We suppose that the equivalence class contains four sensitive details: “tuberculosis” sensitive points. Assuming that the attacker knows that someone is in the equivalence class, the attacker is confident to speculate that the person has the characteristics of “emphysema” disease tendency, which is unacceptable for the patient [ 12 ]. Medical information contains many nonsensitive attribute values such as “flu” or “fever,” and the disclosure of these attribute values will not infringe on individual privacy.…”

Section: Information Entropy L-diversity Modelmentioning

confidence: 99%

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

Khan

Saravanan²,

Gnanaprakasam

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm’s execution time.

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Section: Information Entropy L-diversity Modelmentioning

confidence: 99%

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

Khan

Saravanan²,

Gnanaprakasam

et al. 2022

Computational Intelligence and Neuroscience

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“…The l-diversity anonymous model improves the privacy protection ability of the traditional K-anonymity model and is widely used in the privacy protection of static data publishing. Several improved algorithms based on this model have been well studied in the literature [10][11][12]. However, most of the data publishing methods based on the l-diversity anonymous model adopt the generalization operation on quasi-identifiers.…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Dynamic Data Release Against Synonymous Linkage Based on Micro Aggregation

Yan¹,

Herman²,

Mahmood³

et al. 2021

Preprint

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The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensifified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academic and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance,and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on micro aggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the micro-aggregation anonymous method. The dynamic update program is introduced into the proposed micro-aggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods.

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“…Despite improvement was observed, it did not address the incremental data update issue. To address this, a new probabilistic data structure was proposed (Temuujin et al , 2019) to improve data processing efficiency.…”

Section: Literature Review Of Related Workmentioning

confidence: 99%

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

Sujatha

Udayarani

2021

IJICC

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PurposeThe purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback.Design/methodology/approachIn this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.FindingsAnalysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated.Originality/valueThe paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.

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Efficient L-Diversity Algorithm for Preserving Privacy of Dynamically Published Datasets

Cited by 30 publications

References 23 publications

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

Privacy Preserving Dynamic Data Release Against Synonymous Linkage Based on Micro Aggregation

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

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