Optimization Approach for Detecting the Critical Data on a Database

TL;DR

This paper presents an optimization-based method to identify critical data segments in databases by analyzing malicious transaction patterns and applying graph measures, applicable to various complex graph structures.

Contribution

It introduces a universal optimization approach for detecting critical database elements across diverse graph types, extending the problem to abstract settings.

Findings

Effective detection of soiled and clean segments in database graphs.

Applicable to directed, weighted, disconnected, cyclic graphs.

Generalizes critical data detection to abstract graph settings.

Abstract

Through purposeful introduction of malicious transactions (tracking transactions) into randomly select nodes of a (database) graph, soiled and clean segments are identified. Soiled and clean measures corresponding those segments are then computed. These measures are used to repose the problem of critical database elements detection as an optimization problem over the graph. This method is universally applicable over a large class of graphs (including directed, weighted, disconnected, cyclic) that occur in several contexts of databases. A generalization argument is presented which extends the critical data problem to abstract settings.