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sis 24(2):

Research Article

Digital Forensic Framework for Protecting Data Privacy during Investigation

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  • @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
Suvarna Chaure1,*, Vanita Mane1
  • 1: Ramrao Adik Institute of Technology
*Contact email: suvarnakendre@gmail.com

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.

Keywords
Privacy preservation, Digital Forensics, Machine Learning
Received
2023-06-11
Accepted
2023-08-28
Published
2023-09-27
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.4002

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.

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