Security aware index based quasi–identifier approach for privacy preservation of data sets for cloud applications

Sudhakar, Rayapati Venkata; Rao, T. Ch. Malleswara

doi:10.1007/s10586-019-03028-7

Cited by 23 publications

(9 citation statements)

References 17 publications

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“…In 2020, Sudhakar et al 24 have proposed a proficient “index oriented quasi‐identifier approach” that ensured privacy and achieved higher data utility over distributed and incremental data sets. The updated FCM approach was exploited for forming the similarity‐based clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

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Hybrid optimization‐based privacy preservation of database publishing in cloud environment

Doss

Kamalakkannan

2022

Concurrency and Computation

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This work intends to introduce an optimization-based privacy preservation model via selecting the optimal key matrix. Here, privacy preservation is carried out under two processes, namely, "data sanitization and restoration." In fact, data sanitization is the data preservation method, where the data (message) are preserved using the optimal key. Similarly, data restoration is the inverse procedure of sanitization. Here, the key matrix is optimally chosen using a novel hybrid algorithm. For optimization purpose, this work deploys a hybrid optimization approach known as random-based grey dragon algorithm (R-GDA) that involves the concepts of both "dragonfly algorithm (DA) and grey wolf optimization (GWO) algorithm." The novelty of the work is introduced in hybrid optimization approach R-GDA. Eventually, the supremacy of the adopted method is validated over other existing approaches in terms of various measures such as privacy, utility, and so on. The privacy preservation in the cloud is achievable in the field of education, banking sector, military, and the research community.

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Section: Literature Reviewmentioning

confidence: 99%

“…However, it needs exploration on decryption outsourcing models. FCM algorithm adopted in Reference 24 is very much efficient and offers reduced processing time. Nevertheless, cluster similarity was formulated only for arithmetical data.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid optimization‐based privacy preservation of database publishing in cloud environment

Doss

Kamalakkannan

2022

Concurrency and Computation

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“…The research of privacy-preserving outsourced data focuses on anonymization-based methods [12][13][14][15][16][17][18], cryptographicbased methods [19][20][21][22][23][24], hybrid methods [2,[25][26][27], and methods that seek to improve the data utility [26,28,29]. Some recent studies have demonstrated the privacy requirements of incremental datasets [30][31][32] and multiple sensitive attributes [33][34][35]. However, most of these studies neglected the issue of identification of the right QIDs, despite its importance in the success of the anonymity process.…”

Section: Related Workmentioning

confidence: 99%

Quasi‐Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

Mansour

Siraj

Ghaleb

et al. 2021

Wireless Communications and Mobile Computing

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Cloud computing plays an essential role as a source for outsourcing data to perform mining operations or other data processing, especially for data owners who do not have sufficient resources or experience to execute data mining techniques. However, the privacy of outsourced data is a serious concern. Most data owners are using anonymization-based techniques to prevent identity and attribute disclosures to avoid privacy leakage before outsourced data for mining over the cloud. In addition, data collection and dissemination in a resource-limited network such as sensor cloud require efficient methods to reduce privacy leakage. The main issue that caused identity disclosure is quasi-identifier (QID) linking. But most researchers of anonymization methods ignore the identification of proper QIDs. This reduces the validity of the used anonymization methods and may thus lead to a failure of the anonymity process. This paper introduces a new quasi-identifier recognition algorithm that reduces identity disclosure which resulted from QID linking. The proposed algorithm is comprised of two main stages: (1) attribute classification (or QID recognition) and (2) QID dimension identification. The algorithm works based on the reidentification of risk rate for all attributes and the dimension of QIDs where it determines the proper QIDs and their suitable dimensions. The proposed algorithm was tested on a real dataset. The results demonstrated that the proposed algorithm significantly reduces privacy leakage and maintains the data utility compared to recent related algorithms.

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“…A cross-layer and subject logic-based dynamic reputation (CLSL-DR) mechanism was proposed (Meganathan and Palanichamy, 2015) to ensure security and privacy. An efficient index-based quasi-identifier model was designed (Sudhakar and Rao, 2020) to attain high data utility and also preserve privacy to a greater extent by means of modified fuzzy c means. A deep learning model using Bayesian functions was proposed (Zhong and Xiao, 2017) to optimize the efficiency of privacy preservation.…”

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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Security aware index based quasi–identifier approach for privacy preservation of data sets for cloud applications

Cited by 23 publications

References 17 publications

Hybrid optimization‐based privacy preservation of database publishing in cloud environment

Hybrid optimization‐based privacy preservation of database publishing in cloud environment

Quasi‐Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

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

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