Instructive of Ooze Information

We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data are leaked and bring into being in an unconstitutional place (e.g., on the web or somebody2019;s laptop). The distributor must evaluate the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. We propose data distribution strategies (across the agents) that improve the likelihood of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject 201C;realistic but replica201D; data records to further improve our chances of detecting leakage and identifying the guilty party. In the course of doing business, sometimes sensitive data must be handed over to supposedly trusted third parties. For example, a hospital may give patient records to Researchers who will devise new treatments. Similarly, a company may have partnerships with other companies that require sharing customer data. Another enterprise may outsource its data processing, so data must be given to various other companies. There always remains a risk of data getting leaked from the agent. Perturbation is a very valuable technique where the data are modified and made 201C;less sensitive201D; before being handed to agents. For example, one can add random noise to certain attributes, or one can replace exact values by ranges. But this technique requires modification of data. Leakage detection is handled by watermarking, e.g., a unique code is implanted in each distributed copy. If that copy is later discovered in the hands of an unconstitutional party, the leaker can be identified. But again it requires code modification. Watermarks can sometimes be destroyed if the data recipient is malicious

Detection of Guilt Model for Data Leakage

Abstract Nowadays the data are distributed for the business purposes through trusted agents. There is a chance for leakage of data and can be identified in unauthorized places. The probability of the leaked information came from one or additional agents should be assessed by the distributor, as hostile having been severally gathered by different means. In this paper we propose information allocation ways (across the agents) that improve the likelihood of distinguishing leakages. These ways do not think about alterations of the free information (e.g., watermarks). In some cases, we will additionally inject "realistic but fake" information records to further improve our possibilities of detection escape and distinguishing the problem