A New Approach to Privacy-Preserving Multiple Independent Data Publishing

Hasan, A S M Touhidul; Jiang, Qingshan; Chen, Hui; Wang, Shengrui

doi:10.3390/app8050783

Cited by 29 publications

(58 citation statements)

References 41 publications

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“…Furthermore, optimal anonymization has been demonstrated to be an NP-Hard problem [62]. Moreover, high dimensionality renders this technique ineffective because the identities of the primary record holders can be unmasked by merging the data with either the public or background information [62], [63].…”

Section: ) T-closeness Approachmentioning

confidence: 99%

Comprehensive Survey on Big Data Privacy Protection

et al. 2020

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In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals' sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM. INDEX TERMS Security, big data, privacy protection, privacy-preserving data mining.

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Section: ) T-closeness Approachmentioning

confidence: 99%

Comprehensive Survey on Big Data Privacy Protection

et al. 2020

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“…Several works of privacy-preserving and security issues are extensively studied in different applications. For example, Hasan et al [41] presented a new method for handling the privacy issue of independent published data. Liu and Li [42] mentioned a clustering-based method for anonymity problem in IoT devices.…”

Section: Privacy-preserving Data Miningmentioning

confidence: 99%

A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

2019

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Privacy-preserving data mining (PPDM) has become an interesting and emerging topic in recent years because it helps hide confidential information, while allowing useful knowledge to be discovered at the same time. Data sanitization is a common way to perturb a database, and thus sensitive or confidential information can be hidden. PPDM is not a trivial task and can be concerned an Non-deterministic Polynomial-time (NP)-hard problem. Many algorithms have been studied to derive optimal solutions using the evolutionary process, although most are based on straightforward or single-objective methods used to discover the candidate transactions/items for sanitization. In this paper, we present a multi-objective algorithm using a grid-based method (called GMPSO) to find optimal solutions as candidates for sanitization. The designed GMPSO uses two strategies for updating gbest and pbest during the evolutionary process. Moreover, the pre-large concept is adapted herein to speed up the evolutionary process, and thus multiple database scans during each evolutionary process can be reduced. From the designed GMPSO, multiple Pareto solutions rather than single-objective algorithms can be derived based on Pareto dominance. In addition, the side effects of the sanitization process can be significantly reduced. Experiments have shown that the designed GMPSO achieves better side effects than the previous single-objective algorithm and the NSGA-II-based approach, and the pre-large concept can also help with speeding up the computational cost compared to the NSGA-II-based algorithm.

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“…Hassan et al [5] described a model to preserve the confidentiality on multiple independent data publishing. This model concentrates on the new direction and introduces a composition attack.…”

Section: Related Workmentioning

confidence: 99%

A Novel Dynamic KCi - Slice Publishing Prototype for Retaining Privacy and Utility of Multiple Sensitive Attributes

Raju¹,

Seetaramanath²,

Rao³

2019

IJITCS

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Data publishing plays a major role to establish a path between current world scenarios and next generation requirements and it is desirable to keep the individuals privacy on the released content without reducing the utility rate. Existing KC and KC i models concentrate on multiple categorical sensitive attributes. Both these models have their own merits and demerits. This paper proposes a new method named as novel KC islice model, to enhance the existing KC i approach with better utility levels and required privacy levels. The proposed model uses two rounds to publish the data. Anatomization approach is used to separate the sensitive attributes and quasi attributes. The first round uses a novel approach called as enhanced semantic l-diversity technique to bucketize the tuples and also determine the correlation of the sensitive attributes to build different sensitive tables. The second round generates multiple quasi tables by performing slicing operation on concatenated correlated quasi attributes. It concatenate the attributes of the quasi tables with the ID's of the buckets from the different sensitive tables and perform random permutations on the buckets of quasi tables. Proposed model publishes the data with more privacy and high utility levels when compared to the existing models.

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A New Approach to Privacy-Preserving Multiple Independent Data Publishing

Cited by 29 publications

References 41 publications

Comprehensive Survey on Big Data Privacy Protection

Comprehensive Survey on Big Data Privacy Protection

A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

A Novel Dynamic KCi - Slice Publishing Prototype for Retaining Privacy and Utility of Multiple Sensitive Attributes

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