Data Leakage Detection Project Abstract

Introduction to Data Leakage Detection Project :

In this paper we will see how data how data leakage occurs and the preventive measures to reduce the data leakage. Data leakages occur when the sensitive are handed over to the trusted third parties and due to this leakage occurs and the data may be found at the unauthorized persons. Perturbation is the most effective technique use for reducing data leakages. In this paper we will look into the unobtrusive techniques for detecting the data leakages.

Leakage prevention techniques:

Perturbation is the effective technique in which the data to be handed over is modified and made less sensitive so that the leakage does not occur. Water marking method is traditionally used in which a unique code is embedded in the data so as to identify the data leakage.

This model is used for accessing the guilt of agents and algorithm is used for distributed the objects to the agents. We will use agent guilt model analysis which estimates the values in the objects for guessing the targets. By using the component failures are known and by using this it checks the location of the leaked agent. To check the parameters interaction with the scenarios generated guilt model analysis is used.

Conclusions:

When there is need for the data to be handover to the other agents then we can use the watermark technique for the data, for finding the leakages. If there are more intended users there it will be difficult for us to know from which agent the data leakage has occurred by using watermark technique. The proposed algorithm employs various data distribution strategies for finding the leaker which ensures safety for the distributer.