An Target-Based Privacy-Preserving Approach Using Collaborative Filtering and Anonymization Technique

Chaudhary, Priyanka; Suthar, Krunal; Patel, Kalpesh

doi:10.1007/978-981-15-0222-4_57

Cited by 1 publication

(2 citation statements)

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“…to learn the collaboration link between all datasets and maps. Its value is in the interval [1][2][3][4][5][6][7][8][9][10], 1 -for the neutral link, when no importance to collaboration is given, and 10 for the maximal collaboration within a map. Its value changes for each iteration during the collaboration step.…”

Section: Algorithm 1: the Topological Collaborative Multi-view Algorithmmentioning

confidence: 99%

“…The growing interest in data anonymization was mainly motivated by the desire of governments and institutions to open their data as a proof of democracy and good practices. Open data is a very promising study field and it is very challenging because the data released must be anonymized forever with very low re-identification rate and should ensure sufficient quality for the analytics [7,31]. Aware of the importance of the balance between privacy and utility, many approaches were introduced to tackle this problem, the first approaches were mainly based on the randomization method which consists of adding noise to data [1].…”

Section: Introductionmentioning

confidence: 99%

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Data Anonymization through Collaborative Multi-view Microaggregation

Zouinina

Bennani²,

Rogovschi

et al. 2020

Journal of Intelligent Systems

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The interest in data anonymization is exponentially growing, motivated by the will of the governments to open their data. The main challenge of data anonymization is to find a balance between data utility and the amount of disclosure risk. One of the most known frameworks of data anonymization is k-anonymity, this method assumes that a dataset is anonymous if and only if for each element of the dataset, there exist at least k − 1 elements identical to it. In this paper, we propose two techniques to achieve k-anonymity through microaggregation: k-CMVM and Constrained-CMVM. Both, use topological collaborative clustering to obtain k-anonymous data. The first one determines the k levels automatically and the second defines it by exploration. We also improved the results of these two approaches by using pLVQ2 as a weighted vector quantization method. The four methods proposed were proven to be efficient using two data utility measures, the separability utility and the structural utility. The experimental results have shown a very promising performance.

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