
Research Article
Digital Forensic Framework for Protecting Data Privacy during Investigation
@ARTICLE{10.4108/eetsis.4002, author={Suvarna Chaure and Vanita Mane}, title={Digital Forensic Framework for Protecting Data Privacy during Investigation}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={11}, number={2}, publisher={EAI}, journal_a={SIS}, year={2023}, month={9}, keywords={Privacy preservation, Digital Forensics, Machine Learning}, doi={10.4108/eetsis.4002} }
- Suvarna Chaure
Vanita Mane
Year: 2023
Digital Forensic Framework for Protecting Data Privacy during Investigation
SIS
EAI
DOI: 10.4108/eetsis.4002
Abstract
Rapid technological breakthroughs, a surge in the use of digital devices, and the enormous amount of data that these devices can store continuously put the state of digital forensic investigation to the test. The prevention of privacy breaches during a digital forensic investigation is a significant challenge even though data privacy protection is not a performance metric. This research offered solutions to the problems listed above that centre on the efficiency of the investigative process and the protection of data privacy. However, it’s still an open problem to find a way to shield data privacy without compromising the investigator's talents or the investigation's overall efficiency. This system proposes an efficient digital forensic investigation process which enhances validation, resulting in more transparency in the inquiry process. Additionally, this suggested approach uses machine learning techniques to find the most pertinent sources of evidence while protecting the privacy of non-evidential private files.
Copyright © 2023 S. Chaure et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.