Data Leakage Detection
Panagiotis Papadimitriou, Member, IEEE, Hector Garcia-Molina, Member, IEEE Abstract—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 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 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. Index Terms—allocation strategies, data leakage, data privacy, fake records, leakage model
1 I NTRODUCTION 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. We call the owner of the data the distributor and the supposedly trusted third parties the agents. Our goal is to detect when the distributor’s sensitive data has been leaked by agents, and if possible to identify the agent that leaked the data. We consider applications where the original sensitive data cannot be perturbed. Perturbation is a very useful technique where the data is modiﬁed and made “less sensitive” before being handed to agents. For example, one can add random noise to certain attributes, or one can replace exact values...