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دسته بندی:
داده کاوی - data mining
سال انتشار:
2018
عنوان انگلیسی مقاله:
Data sanitization in association rule mining: An analytical review
ترجمه فارسی عنوان مقاله:
پاکسازی داده در کاوش قانون انجمنی : یک مرور تحلیلی
منبع:
Sciencedirect - Elsevier - Expert Systems With Applications, 96 (2018) 406-426: doi:10:1016/j:eswa:2017:10:048
نویسنده:
Akbar Telikani, Asadollah Shahbahrami∗
چکیده انگلیسی:
Association rule hiding is the process of transforming a transaction database into a sanitized version to
protect sensitive knowledge and patterns. The challenge is to minimize the side effects on the sanitized
database. Many different sanitization algorithms have been proposed to reach this purpose. This article
presents a structured analysis and categorization of the existing challenges and directions for state-of-the
art sanitization algorithms, with highlighting about their characteristics. Fifty-four scientific algorithms,
primarily spanning the period 2001–2017, were analyzed and investigated in terms of four aspects in
cluding hiding strategy, sanitization technique, sanitization approach, and selection method. In terms of
results and findings, this review showed that (i) in comparison to other aspects of sanitization algorithms,
the transaction and item selection methods more significantly influence the optimality of hiding process,
(ii) blocking technique increases the disclosure risk while distortion technique is better in knowledge
protection field, and transaction deletion/insertion technique is a new direction, (iii) heuristic-based al
gorithms have attracted more attention than other algorithms, especially in the context of hiding the
association rules, (iv) a new trend is to use evolutionary paradigm for knowledge hiding that is often
integrated with the transaction deletion/insertion technique, and (V) hiding the association rules intro
duces more challenges than hiding the frequent itemsets in terms of the determination of strategy and
formulation of the selection method. This study aims to help researchers and database administrators
find recent developments in association rule hiding.
Keywords: Privacy preserving in data mining ، Association rule mining ، Association rule hiding ، Data sanitization
قیمت: رایگان
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