A Model for Data Leakage Detection
Source: Stanford University
The authors study the following problem: a data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data is leaked and found in an unauthorized place (e.g., on the web or somebody's laptop). The distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to have been independently gathered by other means. They propose data allocation strategies (across the agents) that improve the probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases they can also inject "Realistic but fake" data records to further improve the chances of detecting leakage and identifying the guilty party.