Data Linkage and Leakage Detection in Data Mining Using E-Random and S-Random

Sreedhar Ambala, Mamidala Sagar, K. Lavanya


A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data has leaked and found in an unauthorized place (e.g., o n the web or somebody’s

laptop). The distributor should assess the likelihood of the leaked data came from one or more agents, as

opposed to having independently gathered by others. We propose data allocation strategies (across the agents) that improve the probability of identifying leakages and linkage the data. These methods don’t 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. The main objective of this paper E-Random algorithm and S-Random algorithm is used by adding additional information (i.e. Fake data) to the original data to detect the abnormal access in database records very effectively.


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