کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4955397 1364622 2018 14 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
A graph-based multifold model for anonymizing data with attributes of multiple types
کلمات کلیدی
Privacy protection; Data publishing; Transactional data; Uncertain graph; High-dimensional data; Anonymization;
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر شبکه های کامپیوتری و ارتباطات
پیش نمایش صفحه اول مقاله
A graph-based multifold model for anonymizing data with attributes of multiple types
چکیده انگلیسی

Transactional data with attributes of multiple types may be extremely useful to secondary analysis (e.g., learning models and finding patterns). However, anonymization of such data is challenging because it contains multiple types of attributes (e.g., relational and set-valued attributes). Existing privacy-preserving techniques are not applicable to address this problem. In this paper, we propose a novel graph-based multifold model to anonymize data with attributes of multiple types. Under this model, such data are modelled as a graph, and multifold privacy is guaranteed through fuzzing on sensitive attributes and converting associations among items into an uncertain form. Specifically, we define a multi-objective attack model in a graph and devise a safety parameter and algorithm to prevent such attacks. Experiments have been performed on real-life data sets to evaluate the performance.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Security - Volume 72, January 2018, Pages 122-135
نویسندگان
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