An efficient clustering method for k-anonymization

Lin, Jun-Lin; Wei, Meng-Cheng

doi:10.1145/1379287.1379297

Cited by 70 publications

(30 citation statements)

References 11 publications

(17 reference statements)

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“…Several anonymization approaches have been proposed in the literature [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] to preserve the privacy and enhance the data utility. The -diversity model [13] is one of the widely used anonymization based approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, clustering based anonymization approaches such as systematic clustering for the -diversity model [5], greedy k-member clustering algorithm [6], Mondrian algorithm [7], Loukides and Shao [8], weighted featuremeans clustering algorithm [9], one pass -mean clustering algorithm [10], and systematic clustering approach foranonymity [11] were suggested. However, these anonymization based clustering approaches could not be able to produce an optimal cluster.…”

Section: Related Workmentioning

confidence: 99%

“…Second approach calculates the amount of data utility loss via data anonymization. It includes metrics such as information loss metrics [6,10,11]. In the first approach, if the original data has lower data utility then the anonymized data has lower data utility.…”

Section: Information Lossmentioning

confidence: 99%

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A Clustering Approach for the l-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm

Bhaladhare¹,

Jinwala

2014

Advances in Computer Engineering

113

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In privacy preserving data mining, the -diversity and -anonymity models are the most widely used for preserving the sensitive private information of an individual. Out of these two, -diversity model gives better privacy and lesser information loss as compared to the -anonymity model. In addition, we observe that numerous clustering algorithms have been proposed in data mining, namely, -means, PSO, ACO, and BFO. Amongst them, the BFO algorithm is more stable and faster as compared to all others exceptmeans. However, BFO algorithm suffers from poor convergence behavior as compared to other optimization algorithms. We also observed that the current literature lacks any approaches that apply BFO with -diversity model to realize privacy preservation in data mining. Motivated by this observation, we propose here an approach that uses fractional calculus (FC) in the chemotaxis step of the BFO algorithm. The FC is used to boost the computational performance of the algorithm. We also evaluate our proposed FC-BFO and BFO algorithms empirically, focusing on information loss and execution time as vital metrics. The experimental evaluation shows that our proposed FC-BFO algorithm derives an optimal cluster as compared to the original BFO algorithm and existing clustering algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Clustering Approach for the l-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm

Bhaladhare¹,

Jinwala

2014

Advances in Computer Engineering

113

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“…Moreover, the k-anonymization problem is viewed as a clustering problem. Clustering-based approaches [7,27,16,34] search a cluster that has k-records. In full-domain generalization, there are two heuristic approaches for generalization algorithms: the top-down approach and the bottom-up approach.…”

Section: Related Workmentioning

confidence: 99%

PrivacyFrost2: A Efficient Data Anonymization Tool Based on Scoring Functions

Kiyomoto

Miyake

2014

Advanced Information Systems Engineering

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Abstract. In this paper, we propose an anonymization scheme for generating a k-anonymous and l-diverse (or t-close) table, which uses three scoring functions, and we show the evaluation results for two different data sets. Our scheme is based on both top-down and bottom-up approaches for full-domain and partial-domain generalization, and the three different scoring functions automatically incorporate the requirements into the generated table. The generated table meets users' requirements and can be employed in services provided by users without any modification or evaluation.

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“…The paper titled "An Efficient Clustering Method for k-Anonymization" [6] proposed a new clustering method for k-anonymization. The authors argued that in order to minimize the information loss due to anonymization, it is crucial to group similar data together and then anonymize each group individually.…”

Section: K-anonymization and Its Applicationsmentioning

confidence: 99%