Evaluating the Probability of Accuse in Deliberate Data Breaches

Abstract

For many businesses, the quantity of sensitive information handled by external service providers is on the rise. With an expanding array of entities granted access to databases, the task of preventing and tracking data leaks becomes increasingly challenging. We address the problems associated with verifying ownership and unlawful distribution (leaking) of information in relational databases. Our suggested method provides a thorough strategy for identifying, discouraging, and tracking down data breaches in relational databases. Our primary descriptive use case is business process outsourcing scenarios, but our techniques can also be applied to other scenarios involving shared relational databases that need to maintain confidentiality and authenticity. Initially, fingerprinting and watermarking were employed to determine who owns what and keep track of illegally distributed numerical relational data. In our current work, we introduce "realistic but counterfeit" objects into our database, a technique akin to watermarking.