Data Leakage Detectionusing Distribution Method
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2014, Vol 4, Issue 1
Abstract
Modern business activities rely on extensive email exchange. Email leakages have become widespread, and the severe damage caused by such leakages constitutes a disturbing problem for organizations. We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). If the data distributed to third parties is found in a public/private domain then finding the guilty party is a nontrivial task to distributor. Traditionally, this leakage of data is handled by water marking technique which requires modification of data. If the watermarked copy is found at some unauthorized site then distributor can claim his ownership. To overcome the disadvantages of using watermark [2], data allocation strategies are used to improve the probability of identifying guilty third parties. The distributor must assess the likelihood that the leaked came from one or more agents, as opposed to having been independently gathered by other means. In this project, we implement and analyze a guilt model that detects the agents using allocation strategies without modifying the original data. The guilty agent is one who leaks a portion of distributed data. We propose data allocation strategies that improve the probability of identifying leakages. In some cases we can also inject “realistic but fake” data record to further improve our changes of detecting leakage and identifying the guilty party. The algorithms implemented using fake objects will improve the distributor chance of detecting guilty agents. It is observed that by minimizing the sum objective the chance of detecting guilty agents will increase. We also developed a framework for generating fake objects.
Authors and Affiliations
Anis Syed
Keywords
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