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篇名 |
A (k, p)-anonymity Framework to Sanitize Transactional Database with Personalized Sensitivity
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並列篇名 | A (k, p)-anonymity Framework to Sanitize Transactional Database with Personalized Sensitivity |
作者 | Binbin Zhang、Jerry Chun-Wei Lin、Qiankun Liu、Philippe Fournier-Viger、Youcef Djenouri |
英文摘要 | In recent years, analyzing transactional data has become an important data analytic task since it can discover important information in several domains, for recommendation, prediction, and personalization. Nonetheless, transactional data sometimes contains sensitive and confidential information such as personal identifiers, information aboutsexual orientations, medical diseases, and religious beliefs. Such information can be analyzed using various data mining algorithms, which may cause security threats to individuals. Several algorithms were proposed to hide sensitive information in databases but most of them assume that sensitive information is the same for all users, which is an unrealistic assumption. Hence, this paper presents a (k, p)-anonymity framework to hide personal sensitive information. The developed ANonymity for Transactional database (ANT) algorithm can hide multiple pieces of sensitive information in transactions. Besides, it let users assign sensitivity values to indicate how sensitive each piece of information is. The designed anonymity algorithm ensures that the percentage of anonymized data does not exceed a predefined maximum sensitivity threshold. Results of several experiments indicate that the proposed algorithm outperforms thestate- of-the-art PTA and Gray-TSP algorithms in terms of information loss and runtime. |
起訖頁 | 801-808 |
關鍵詞 | Anonymization、Cluster、Multiple sensitive information、Hierarchical attributes |
刊名 | 網際網路技術學刊 |
期數 | 201905 (20:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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