We study the following problem: A data
distributor has given sensitive data to a set of supposedly trusted agents
(thirdparties). Some of the data are 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
having been independently gathered by other means. We 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, we can also inject “realistic but fake” data
records to further improve our chances of detecting leakage and identifying the
guilty party.
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