The currently-emerging technology demands sharing of data using various channels via the Internet, disks, etc. Some recipients of this data can also become traitors by leaking the important data. As a result, the data breaches due to data leakage are also increasing. These breaches include unauthorized distribution, duplication, and sale. The identification of a guilty agent responsible for such breaches is important for: (i) punishing the culprit; and (ii) preventing the innocent user from accusation and punishment. Fingerprinting techniques provide a mechanism for classifying the guilty agent from multiple recipients and also help to prevent the innocent user from being accused of the data breach. To those ends, in this paper, a novel fingerprinting framework has been proposed using a biometric feature as a digital mark (signature). The use of machine learning has also been introduced to make this framework intelligent, particularly for preserving the data usability. An attack channel has also been used to evaluate the robustness of the proposed scheme. The experimental study was also conducted to demonstrate that the proposed technique is robust against several malicious attacks, such as subset selection attacks, mix and match attacks, collusion attacks, deletion attacks, insertion attacks, and alteration attacks.

中文翻译：

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关系数据库的指纹用于阻止数据盗窃

当前出现的技术要求通过Internet，磁盘等使用各种渠道共享数据。这些数据的某些接收者也可能通过泄漏重要数据而成为叛徒。结果，由于数据泄漏引起的数据泄露也越来越多。这些违规行为包括未经授权的分发，复制和销售。查明应对此类违法行为负责的犯罪代理人，对于以下方面很重要：（i）惩罚罪魁祸首；（ii）防止无辜使用者受到指责和惩罚。指纹技术提供了一种对来自多个收件人的有罪代理进行分类的机制，并且还有助于防止无辜的用户被指控数据泄露。为此，在本文中，已经提出了使用生物特征作为数字标记（签名）的新颖指纹识别框架。还引入了机器学习的使用，以使该框架变得智能化，尤其是在保留数据可用性方面。攻击通道也已用于评估所提出方案的鲁棒性。还进行了实验研究以证明所提出的技术对几种恶意攻击具有鲁棒性，例如子集选择攻击，混合匹配攻击，共谋攻击，删除攻击，插入攻击和更改攻击。