Data provenance for cloud forensic investigations, security, challenges, solutions and future perspectives: A survey

Abstract

It is extremely difficult to track down the original source of sensitive data from a variety of sources in the cloud during transit and processing. For instance, data provenance, which records the origins of data, and the record of data usage, update and processing can be introduced to trace malicious vulnerabilities. Thus, data provenance process makes it easy to monitor the sources and causes of any problems in cloud computing. However, data provenance is one of the most prominent drawbacks in cloud storage. Despite many studies, a full assessment of data provenance in cloud forensics is still missing from the literature, especially in wireless sensor networks, blockchain, Internet of Things (IoT), security and privacy. Importantly, one of the major challenges in data provenance is “how to reduce the complexity of evidence.“ That is, ensuring volatile data is captured before being overwritten. Hence, this study presents a survey of recent data provenance problems in cloud computing, provenance taxonomy, and security issues. It also, discusses how volatile data can be captured before being overwritten and then helps identify current provenance limitations and future directions for further study. More also, it examined how data is collected as evidence for digital crime in a real-world scenario. Furthermore, future work in digital provenance for cloud forensics, wireless sensor network, IoT, and blockchain is recommended.