A Probabilistic Approach for Guilty Agent Detection using Bigraph after Distribution of Sample Data
Ishu Gupta, Ashutosh Kumar Singh · Procedia Computer Science · 2018
In today’s on-growing world, there is need to share the information within or outside the enterprise which includes the sensitive information of the enterprise also. For example: companies has to share its sensitive information with its partners, employees and various other entities. This sensitive data can be leaked by third party. Later on, distributor finds the leaked documents at some unauthorized place (eg. through legal discovery process, on user’s drive or the web). We propose a model which assesses the likelihood that the data has been leaked by one or more agents or it has been independently gathered by some other means. The goal of the model is to protect the sensitive information by detecting the leakage and identifying the leaker responsible for data leakage.