Looking Inside the Black-Box: Capturing Data Provenance Using Dynamic Instrumentation

Knowing the provenance of a data item helps in ascertaining its trustworthiness. Various approaches have been proposed to track or infer data provenance. However, these approaches either treat an executing program as a black-box, limiting the fidelity of the captured provenance, or require developers to modify the program to make it provenance-aware. In this paper, we introduce DataTracker, a new approach to capturing data provenance based on taint tracking, a technique widely used in the security and reverse engineering fields. Our system is able to identify data provenance relations through dynamic instrumentation of unmodified binaries, without requiring access to, or knowledge of, their source code. Hence, we can track provenance for a variety of well-known applications. Because DataTracker looks inside the executing program, it captures high-fidelity and accurate data provenance.