L-Diversity Based Dynamic Update for Large Time-Evolving Microdata

Sun, Xiaoxun; Wang, Hua; Li, Jiuyong

doi:10.1007/978-3-540-89378-3_47

Cited by 6 publications

(9 citation statements)

References 13 publications

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“…However, they also considered only insertion operations. Additionally, Sun et al [20] investigated maintaining l-diversity for dynamic data publishing and proposed a solution for data additions and deletions. Their algorithm used a generalization operation to ensure anonymization and focused on information loss thereby maintaining efficiency.…”

Section: Privacy-preservation Of Dynamically Evolving Datasets Andmentioning

confidence: 99%

“…Herein, we describe the conventional l-diversity-based method of preserving privacy of dynamically evolving datasets. To the best of our knowledge, this method [20] is the only one described in the literature for preserving privacy of dynamically evolving datasets undergoing record, deletions and insertions. Thus, we compared experimental results obtained using our proposed method to results obtained for the published one.…”

Section: B Conventional L-diversity-based Algorithm For Preserving Pmentioning

confidence: 99%

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

Temuujin

Ahn

2019

IEEE Access

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Although most conventional methods of preserving data privacy focus on static datasets, which remain unchanged after processing, real-world datasets may be dynamically modified often. Therefore, privacy-preservation methods must maintain data privacy after dataset modification. Re-anonymization of entire datasets is inefficient when large datasets are frequently modified. Although several previous studies have addressed data privacy for incremental data updates (i.e., record insertions), they have not adequately it for dynamic changes made to existing datasets (i.e., record updates and deletions). Therefore, we identified limitations of data-privacy preservation for dynamically evolving datasets and used anatomy instead of generalization and suppression to develop a more efficient l-diversity algorithm for preserving privacy of such datasets. We also used a Cuckoo filter, a new probabilistic data structure for approximate set-membership tests, to improve data-processing efficiency. Experimental results demonstrated that our proposed data-anonymization algorithm processed data more efficiently than other conventional algorithms, requiring much less running time than conventional re-anonymization of entire datasets. The Cuckoo-filtered algorithm was especially efficient, dramatically reducing operation execution times while maintaining privacy of dynamically evolving datasets.

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Section: Privacy-preservation Of Dynamically Evolving Datasets Andmentioning

confidence: 99%

Section: B Conventional L-diversity-based Algorithm For Preserving Pmentioning

confidence: 99%

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

Temuujin

Ahn

2019

IEEE Access

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“…The same argument also could apply to generalization levels. Generalization refers of replacing the actual value of the attribute with a less specific, more general value which is faithful to the original [18][19][20]. For example, the name 'Carol Jones' can be generalized to a less specific value 'C.…”

Section: Authorization Specificationmentioning

confidence: 99%

Privacy-aware access control with trust management in web service

Sun

Wang

et al. 2011

World Wide Web

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With the significant development of mobile commerce, privacy becomes a major concern for both customers and enterprises. Although data generalization can provide significant protection of an individual's privacy, over-generalized data may render data of little value or useless. In this paper, we devise generalization boundary techniques to maximize data usability while, minimizing disclosure of privacy. Inspired by the fact that the permissible generalization level results in a much finer level access control, we propose a privacy-aware access control model in web service environments. We also analyze how to manage a valid access process through a trust-based decision and ongoing access control policies. The extensive experiments on both real-world and synthetic data sets show that the proposed privacy aware access control model is practical and effective.

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“…Several recent studies [7,22,20,19] have aimed at modeling and integrating background knowledge in data anonymization. Second, several works [6,39,28] considered continual data publishing, i.e., re-publication of the data after it has been updated. A m-invariance is one of the representative models [39].…”

Section: Related Workmentioning

confidence: 99%

“…Generalization replaces lower level domain values with higher level domain values. For example, Age 27, 28 in the lower level can be replaced by the interval (27)(28) in the higher level.…”

Section: Introductionmentioning

confidence: 99%

A family of enhanced -diversity models for privacy preserving data publishing

Sun

Wang

2011

Future Generation Computer Systems

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Privacy preservation is an important issue in the release of data for mining purposes. Recently, a novel l-diversity privacy model was proposed, however, even an l-diverse data set may have some severe problems leading to reveal individual sensitive information. In this paper, we remedy the problem by introducing distinct (l, α)-diversity, which, intuitively, demands that the total weight of the sensitive values in a given QI-group is at least α, where the weight is controlled by a pre-defined recursive metric system. We provide a thorough analysis of the distinct (l, α)-diversity and prove that the optimal distinct (l, α)-diversity problem with its two variants entropy (l, α)-diversity and recursive (c, l, α)-diversity are NP-hard, and propose a top-down anonymization approach to solve the distinct (l, α)-diversity problem with its variants. We show in the extensive experimental evaluations that the proposed methods are practical in terms of utility measurements and can be implemented efficiently.

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L-Diversity Based Dynamic Update for Large Time-Evolving Microdata

Cited by 6 publications

References 13 publications

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

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

Privacy-aware access control with trust management in web service

A family of enhanced -diversity models for privacy preserving data publishing

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