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.

敏感数据泄露是当前企业面临的主要问题之一，本研究提出了一种基于数据统计分析的数据泄露预测和访问权限控制的数据泄露防护解决方案。研究结果表明，该方法具有较高的精确度，在极端数据访问情况下，能够正确地对用户进行分类。