Issue No. 01 - January (2011 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.100
Panagiotis Papadimitriou , Stanford University, Stanford
Hector Garcia-Molina , Stanford University, Stanford
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 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.
Allocation strategies, data leakage, data privacy, fake records, leakage model.
H. Garcia-Molina and P. Papadimitriou, "Data Leakage Detection," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 51-63, 2010.