A Data Anonymization Method to Mitigate Identity Attack in Transactional Database Publishing

Dedi Gunawan
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引用次数: 1

Abstract

Publishing transactional database becomes more recognized for many institutions such as retails and groceries. Many of them share or publish their data to other institutions as an effort to gain more revenue for their business. However, publishing such a database is problematic since irresponsible parties may associate records in database with specific individuals to disclose personal identity known as identity attack. Data anonymization is an effective technique to protect database from the threat. Unfortunately, applying data anonymization method in transaction database using generalization and suppression based techniques may reduce data utility significantly and cause severe distortion to database properties. A solution to mitigate such drawbacks has been proposed by replacing item with another item instead of applying those techniques. However, selecting an item to replace another item causes other problems specifically when the selected item for the replacement process is not the optimum one. Therefore, in this paper we propose a data anonymization method which performs item replacement that utilizes weighted scoring method to select an optimal item with respect to minimize information loss and maintaining database properties. Experimental results show that the proposed method guarantee higher privacy protection compared with an existing method and it successfully generates an anonymized database while at the same time it maintains data utility by minimizing information loss more than 50% compared with that of an existing method. In addition, the data property of the anonymized database can be well maintained.
一种缓解事务性数据库发布中身份攻击的数据匿名化方法
发布事务性数据库在零售和杂货等许多机构中得到越来越多的认可。他们中的许多人将他们的数据分享或发布给其他机构,以努力为他们的业务获得更多收入。然而,发布这样的数据库是有问题的,因为不负责任的人可能会将数据库中的记录与特定的个人联系起来,以泄露个人身份,称为身份攻击。数据匿名化是保护数据库免受威胁的一种有效技术。然而,在基于泛化和抑制技术的事务数据库中应用数据匿名化方法可能会大大降低数据的利用率,并对数据库属性造成严重的失真。一种缓解这种缺陷的解决方案是用另一项代替另一项,而不是应用这些技术。但是,选择一个项目来替换另一个项目会导致其他问题,特别是当为替换过程选择的项目不是最优的项目时。因此,在本文中,我们提出了一种数据匿名化方法,该方法利用加权计分法来选择一个最优的项目,以最小化信息丢失和维护数据库属性。实验结果表明,与现有方法相比,该方法在保证隐私保护的同时,成功地生成了匿名数据库,同时保持了数据的实用性,使信息丢失比现有方法减少了50%以上。此外,可以很好地维护匿名数据库的数据属性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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