In order to satisfy higher privacy requirements and make it suitable for multi-record publishing datasets, a bidirectional personalized generalization (BP-generalization) model is proposed as a new solution in this paper. The rational is to focus anonymous objects on both relational and set-valued information.
Sep 6, 2019
A bidirectional personalized generalization (BP-generalization) model is proposed as a new solution to satisfy higher privacy requirements and make it ...
Extensive experiments show that our algorithm can effectively protect data privacy and has better comprehensive performance in terms of information loss and ...
An efficient model Heap Bucketization-anonymity (HBA) has been proposed to balance privacy and utility with multiple sensitive attributes. The Heap ...
Acs G, Achara JP, Castelluccia C (2015) Probabilistic km-anonymity efficient anonymization of large set-valued datasets. · Chen Z, Kang H, Yin S, Kim S (2016) An ...
Privacy-preserving model and generalization ... A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive ...
Kanwal et al. [41] proposed a generalization privacy-preserving method suitable for the scenarios of 1:M records (an individual can have multiple records) ...
In this paper, we present a novel PPDC method that lets respondents (clients) use generalization to create anonymous data in the CS2U model. Generalization is ...
We introduce an anti-discrimination model that can cover every possible nuance of dis- crimination w.r.t. multiple attributes, not only for specific protected ...
Composition is the first privacy model to prevent against composition attacks in the multiple independent data publishing context. The proposed method in [23] ...