Sensitive data leakage is the major growing problem being faced by enterprises in this technical era. Data leakage causes severe threats for organization of data safety which badly affects the reputation of organizations. Data leakage is the flow of sensitive data/information from any data holder to an unauthorized destination. Data leak prevention (DLP) is set of techniques that try to alleviate the threats which may hinder data security. DLP unveils guilty user responsible for data leakage and ensures that user without appropriate permission cannot access sensitive data and also provides protection to sensitive data if sensitive data is shared accidentally. In this paper, data leakage prevention (DLP) model is used to restrict/grant data access permission to user, based on the forecast of their access to data. This study provides a DLP solution using data statistical analysis to forecast the data access possibilities of any user in future based on the access to data in the past. The proposed approach makes use of renowned simple piecewise linear function for learning/training to model. The results show that the proposed DLP approach with high level of precision can correctly classify between users even in cases of extreme data access.

翻译：敏感数据泄露是企业在这个技术时代面临的主要且日益严重的问题。数据泄露对组织的数据安全构成严重威胁，并对组织声誉造成恶劣影响。数据泄露是指敏感数据或信息从任何数据持有者流向未授权目标的过程。数据防泄漏（DLP）是一套旨在缓解可能妨碍数据安全的威胁的技术。DLP能够识别出对数据泄露负有责任的恶意用户，确保未经适当许可的用户无法访问敏感数据，并在敏感数据被意外共享时提供保护。本文使用数据防泄漏（DLP）模型，基于对用户数据访问行为的预测，来限制或授予用户数据访问权限。本研究通过数据统计分析，基于用户过去的数据访问行为预测其在未来的数据访问可能性，从而提供一种DLP解决方案。所提出的方法利用著名的简单分段线性函数进行学习/训练建模。结果表明，本文提出的DLP方法具有高精度，即使在极端数据访问情况下也能正确区分不同用户。