Data Leakage Detection
DATA LEAKAGE DETECTION
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Abstract - If a distributor has given sensitive data to a set of supposedly trusted agents (third parties) and if some of the data is leaked and found in an unauthorized place, the distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. The techniques used improves the probability of identifying leakages and finding guilty agent. These methods do not rely on alterations of the released data so authentication for editing will be provided to keep the track of file getting edited. In this “realistic but fake” data objects are injected to further improve the chances of detecting leakage and identifying the guilty party.
Keywords- data leakage, data privacy, fake objects, leakage model, guilty party.
I. INTRODUCTION
1.1 Overview
There always has been a need to transfer sensitive data to any supposedly trusted third parties. For example, a company may have partnership with another company so the transactions may involve sharing customer’s private data. The supposedly trusted third parties are the agents and the owner of the data who sends his sensitive data to the agents is called the distributor. Our goal is to detect when the distributor’s sensitive data have been leaked by agents, and to identify the agent that leaked the data. In a technique called perturbation data is modified and made less sensitive before it is handled to agents. In some applications the original sensitive data cannot be perturbed.
The distributor after sharing his sensitive data objects, which we consider here in form of file, discovers those objects at some unauthorized place. If distributor finds “enough evidence” that an agent leaked data, he may initiate legal proceedings. This model that is being developed will be useful for assessing the “guilt” of agents. Also algorithms are
.
Abstract - If a distributor has given sensitive data to a set of supposedly trusted agents (third parties) and if some of the data is leaked and found in an unauthorized place, the distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. The techniques used improves the probability of identifying leakages and finding guilty agent. These methods do not rely on alterations of the released data so authentication for editing will be provided to keep the track of file getting edited. In this “realistic but fake” data objects are injected to further improve the chances of detecting leakage and identifying the guilty party.
Keywords- data leakage, data privacy, fake objects, leakage model, guilty party.
I. INTRODUCTION
1.1 Overview
There always has been a need to transfer sensitive data to any supposedly trusted third parties. For example, a company may have partnership with another company so the transactions may involve sharing customer’s private data. The supposedly trusted third parties are the agents and the owner of the data who sends his sensitive data to the agents is called the distributor. Our goal is to detect when the distributor’s sensitive data have been leaked by agents, and to identify the agent that leaked the data. In a technique called perturbation data is modified and made less sensitive before it is handled to agents. In some applications the original sensitive data cannot be perturbed.
The distributor after sharing his sensitive data objects, which we consider here in form of file, discovers those objects at some unauthorized place. If distributor finds “enough evidence” that an agent leaked data, he may initiate legal proceedings. This model that is being developed will be useful for assessing the “guilt” of agents. Also algorithms are