A framework for utility enhanced incomplete microdata anonymization

Gong, Qiyuan; Chen, Zhouguo; Wu, Wenjia; Luo, Junzhou

doi:10.1007/s10586-017-0795-6

Cited by 5 publications

(12 citation statements)

References 32 publications

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“…RoA experimentation is performed with four privacy parameters namely Discernibility (DM, Ambiguity (AM), Average Equivalence Class (AEC), Entropy based model (EBM). Tables 9, 10 Tables 9, 10 and 11) can able to generate minimum values of SR C , NVI r and NVR r than [9] and [12]. Enhanced ARX Anonymization -AEC [12] seems to be very sensitive to the null value population.…”

Section: Record Oriented Anonymization (Roa)mentioning

confidence: 97%

“…Tables 9, 10 Tables 9, 10 and 11) can able to generate minimum values of SR C , NVI r and NVR r than [9] and [12]. Enhanced ARX Anonymization -AEC [12] seems to be very sensitive to the null value population. By evaluating all the method with SR C , NVI r and NVR r , two of our proposed methodologies seems to be optimum in RoA i) Enhanced ARX Anonymization-DM and ii) Enhanced ARX Anonymization-AM.…”

Section: Record Oriented Anonymization (Roa)mentioning

confidence: 97%

“…[11] Proposed two methods basic match and extend match based on generalization to anonymize the deficient records but often generates more information loss. In [12] the authors formulated an enhanced Mondrian method, where they split the known recordset into two groups i.e. records with null value and records with normal value with respect to each attributes.…”

Section: Related Workmentioning

confidence: 99%

“…One of the recent literature work [12] developed an enhanced Mondrian and semi partition approach, where they divide the given dataset into null value and normal value set with respect to attribute. The partitions are merged and anonymized in top down strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Each of the dimensional anonymization follows different perception in computing the information loss. (iv) In [12] work top down anonymization is used, whereas in this approach ,null value segregation is appropriately combined with ARX Anonymization often referred to be Enhanced ARX Anonymization-( , ) Anonymity Model to anonymize the recordset, which is capable enough to generate k anonymized data set with increased data utility. (v) The proposed Enhanced ARX Anonymization -( , )Anonymity Model is executed at least with a minimum of two data quality parameters for each of the dimensional oriented approaches to identify the optimistic one.…”

Section: Introductionmentioning

confidence: 99%

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Utility Enhancement of Deficient Relational Recordset Anonymization

Samraj¹,

Appusamy²,

Shankar³

2018

IJIES

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Increase in the use of digital platforms generate a very huge amount of user data which on processing reveals more valuable information about users while mining or it may also reveal some future events. Privacy preserving data mining (PPDM) is the current developing area of research that precisely ensures a certain level of privacy by increasing the utility of information. Data Anonymization is a PPDM technique that protects sensitive information in the recordset with high confidence. Anonymizing recordsets that consists of null values habitually referred as deficient recordsets, suffer from serious information loss owing to null value contamination. This would be arisen because of the null values present in the original recordset. In this paper, we proposed an enhanced ARX framework to anonymize the deficient recordsets through a higher level of privacy and utility. We explore the characteristics of null value contamination on generalization, driven by these characteristics, we formulate an Enhanced ARX Anonymization-(,)Anonymity Model to preserve data utility on deficient recordsets. The results obtained by executing our framework over the real-world dataset proved to be optimized in providing better tradeoff between utility and privacy for deficient recordsets in Cell oriented Anonymization (CoA), Attribute oriented Anonymization (AoA) and Record oriented Anonymization (RoA) than the existing procedures.

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Section: Record Oriented Anonymization (Roa)mentioning

confidence: 97%

Section: Record Oriented Anonymization (Roa)mentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Utility Enhancement of Deficient Relational Recordset Anonymization

Samraj¹,

Appusamy²,

Shankar³

2018

IJIES

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Exposing safe correlations in transactional datasets

Chicha

Bouna

Wünsche

et al. 2021

SOCA

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Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

2022

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With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user's privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.

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A framework for utility enhanced incomplete microdata anonymization

Cited by 5 publications

References 32 publications

Utility Enhancement of Deficient Relational Recordset Anonymization

Utility Enhancement of Deficient Relational Recordset Anonymization

Exposing safe correlations in transactional datasets

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

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