There is an escalating perception in some quarters that the conclusions drawn from digital evidence are the subjective views of individuals and have limited scientific justification. This paper attempts to address this problem by presenting a formal model for reasoning about digital evidence. A Bayesian network is used to quantify the evidential strengths of hypotheses and, thus, enhance the reliability and traceability of the results produced by digital forensic investigations. The validity of the model is tested using a real court case. The test uses objective probability assignments obtained by aggregating the responses of experienced law enforcement agents and analysts. The results confirmed the guilty verdict in the court case with a probability value of 92.7%.
Privacy is a fundamental human right defined in the Universal Declaration of Human Rights. To enable the protection of data privacy, personal data that are not related to the investigation subject should be excluded during computer forensic examination. In the physical world, protection of privacy is controlled and regulated in most countries by laws. Legislation for handling private data has been established in various jurisdictions. In the modern world, the massive use of computers generates a huge amount of private data and there is correspondingly an increased expectation to recognize and respect human rights in digital investigation. However, there does not exist a forensically sound model for protecting private data in the context of digital investigation, and it poses a threat to privacy if the investigation involves the processing of such kind of data. In this paper, we try to address this important issue and present a cryptographic model designed to be incorporated into the current digital investigation framework, thereby adding a possible way to protect data privacy in digital investigation.
No abstract
With the advent of peer-to-peer communication technologies, individuals can easily connect to one another over Internet for file sharing and online chatting. Although these technologies provide wonderful platforms for users to share their digital materials, its illegitimate use on unauthorized sharing of copyrighted files is increasingly rampant. With the BitTorrent (BT) technology, the tracking down of these illegal activities is even more difficult as the downloaders can also act as the distributors and cooperate to provide different parts of the same file for sharing. It is close to impossible for law enforcement agencies to trace these distributed and short-duration Internet piracy activities with limited resources. In this paper, we present the first automated rule-based software system, the BitTorrent Monitoring (BTM) System, for monitoring, recording, and analyzing suspicious BT traffic on the Internet. From a preliminary experiment on a real case, the system successfully located 126 distributors (a.k.a. seeders) for some Cantonese pop songs within 90 minutes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.