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.
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1. Casey E. Digital Evidence and Computer Crime: Forensic Science, Computers and the Internet. Academic Press.
2. Quick D. Privacy-Preserving Data Analysis: A Practical Introduction. Springer. 2019.
3. Pollitt MM. Digital Forensics: Principles and Practices. Auerbach Publications. 2005.
4. Carrier B. A Hypertext History of Multi-User Dimensions. Available online: www.digital-evidence.org/hypertext/HypertextHistoryOfMUDs.pdf (accessed on 15 October 2023).
5. Garfinkel S. Digital Forensics Research: The Next 10 Years. Digital Investigation, 7(1): 3–21. doi: 10.1016/j.diin.2014.08.003
6. Koufaris M. Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior. Information Systems Research, 2002. 13(2): 205–223. doi: 10.1287/isre.2013.0480
7. Wu H. Privacy-Preserving Analysis of Vertically Partitioned Data. IEEE Transactions on Knowledge and Data Engineering. 20(10): 1293–1306. doi: 10.1109/TKDE.2019.2904582, 2008.
8. Schneier B. Applied Cryptography: Protocols, Algorithms, and Source Code in C. John Wiley & Sons. 1996.
9. Abadi M, Chu A, Goodfellow I, et al. Deep Learning with Differential Privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016. doi: 10.1145/2976749.2978318
10. Diffie W, Hellman M. New directions in cryptography. IEEE Transactions on Information Theory. 1976, 22(6): 644–654. doi: 10.1109/tit.1976.1055638
11. Dwork C. Differential Privacy. In: Proceedings of the 33rd International Conference on Automata, Languages and Programming, 2006. https://doi.org/10.1561/0400000042
12. Kahneman D. Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011. doi: 10.1037/1411399
13. Rouse R. Privacy-preserving machine learning over encrypted data. Available online: https://searchsecurity.techtarget.com/definition/privacy-preserving-machine-learning-over-encrypted-data (accessed on 15 October 2023).
14. Swanson M, Broom K. Building a Digital Forensic Laboratory: Establishing and Managing a Successful Facility. Syngress, 2004. doi: 10.1201/9781420055476
15. Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press. 2016.
16. Boneh D, Shacham H. Group Signatures with Verifier-Local Revocation. Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques, 2007. doi: 10.1145/1030083.1030106
17. Zhang P. Privacy-Preserving Federated Learning: Challenges and Solutions. IEEE Internet of Things Journal, 2020. 7(10): 9234–9251. doi: 10.1109/MCE.2019.2959108
18. Kerschbaum F. Efficient Privacy-Preserving Data Aggregation. Proceedings of the 5th ACM workshop on Privacy in electronic society, 2009. doi: 10.1109/ICDE.2007.367893
19. Alom MZ. The history began from AlexNet: A comprehensive survey on deep learning approaches. arXiv 2018. arXiv:1803.01164
20. Tanenbaum AS, Van Steen M. Distributed Systems: Principles and Paradigms. Prentice Hall, 2007.
21. Hardy C. Trust and Trustworthiness. Russell Sage Foundation. 1997.
22. Boyd C, Mathuria A. Protocols for Authentication and Key Establishment. Springer, 2010.
23. Rivest RL, Shamir A, Adleman L. A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 1978. 21(2): 120–126. doi: 10.1145/359340.359342
24. Oh JK. Towards Privacy-Preserving Federated Learning: A Review on Recent Progress. Information Sciences, 2018. 546: 428–441. doi: 10.1109/ICC.2019.8761267
DOI: https://doi.org/10.32629/jai.v7i5.1270
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