Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Data Leakage Detection


Affiliations
1 Department of Computer Science & Engineering, St. Ann's College of Engineering & Technology, Affiliated to JNTU Kakinada, India
2 Department of CSE, St. Ann's College of Engineering & Technology, Affiliated to JNTU Kakinada, India
     

   Subscribe/Renew Journal


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.

Keywords

Allocation Strategies, Data Leakage, Perturbation, Fake Records, Leakage Model.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 235

PDF Views: 2




  • Data Leakage Detection

Abstract Views: 235  |  PDF Views: 2

Authors

Jayavarapu Karthik
Department of Computer Science & Engineering, St. Ann's College of Engineering & Technology, Affiliated to JNTU Kakinada, India
P. Harini
Department of CSE, St. Ann's College of Engineering & Technology, Affiliated to JNTU Kakinada, India

Abstract


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.

Keywords


Allocation Strategies, Data Leakage, Perturbation, Fake Records, Leakage Model.