Designing an automated, privacy preserving, and efficient Digital Forensic Framework

Abstract

The digital forensic investigation field faces continual challenges due to rapid technological advancements, the widespread use of digital devices, and the exponential growth in stored data. Protecting data privacy has emerged as a critical concern, particularly as traditional forensic techniques grant investigators unrestricted access to potentially sensitive data. While existing research addresses either investigative effectiveness or data privacy, a comprehensive solution that balances both aspects remains elusive. This study introduces a novel digital forensic framework that employs case information, case profiles, and expert knowledge to automate analysis. Machine learning techniques are utilized to identify relevant evidence while prioritizing data privacy. The framework also enhances validation procedures, fostering transparency, and incorporates secure logging mechanisms for increased accountability.