Data leakage causes significant losses and privacy breaches worldwide. In this paper we present a white-box data leakage detection system to spot anomalies in database transactions. We argue that our approach represents a major leap forward w.r.t. previous work because: i) it significantly decreases the False Positive Rate (FPR) while keeping the Detection Rate (DR) high; on our experimental dataset, consisting of millions of real enterprise transactions, we measure a FPR that is orders of magnitude lower than in state-of-the-art comparable approaches; and ii) the white-box approach allows the creation of self-explanatory and easy to update profiles able to explain why a given query is anomalous, which further boosts the practical applicability of the system.
|Name||Lecture Notes in Computer Science|
|Conference||28th Annual IFIP WG 11.3 Working ConferenceData and Applications Security and Privacy (DBSec)|
|Period||14/07/14 → 16/07/14|
|Other||July 14-16, 2014|
- Data Security
- Leakage Detection
- Anomaly Detection