# A Comprehensive Survey on the State-of-the-art Data Provenance Approaches for Security Enforcement

arXiv: https://arxiv.org/abs/2107.01678 · HTML: https://mlsanity.com/paper/2107.01678/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [RESTRAIN: Reinforcement Learning-Based Secure Framework for Trigger-Action IoT Environment](https://mlsanity.com/paper/2503.09513/) (2025)

## Abstract (arXiv)

Data provenance collects comprehensive information about the events and operations in a computer system at both application and system levels. It provides a detailed and accurate history of transactions that help delineate the data flow scenario across the whole system. Data provenance helps achieve system resilience by uncovering several malicious attack traces after a system compromise that are leveraged by the analyzer to understand the attack behavior and discover the level of damage. Existing literature demonstrates a number of research efforts on information capture, management, and analysis of data provenance. In recent years, provenance in IoT devices attracts several research efforts because of the proliferation of commodity IoT devices. In this survey paper, we present a comparative study of the state-of-the-art approaches to provenance by classifying them based on frameworks, deployed techniques, and subjects of interest. We also discuss the emergence and scope of data provenance in IoT networks. Finally, we present the urgency in several directions that data provenance needs to pursue, including data management and analysis.