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.
In recent years, peer-to-peer (P2P) applications have become the dominant form of Internet traffic. Foxy, a Chinese community focused filesharing tool, is increasingly being used to disseminate private data and sensitive documents in Hong Kong. Unfortunately, its scattered design and a highly distributed network make it difficult to locate a file originator. This paper proposes an investigative model for analyzing Foxy communications and identifying the first uploaders of files. The model is built on the results of several experiments, which reveal behavior patterns of the Foxy protocol that can be used to expose traces of file originators.
Because of the way computers operate, every discrete event potentially leaves a digital trace. These digital traces must be retrieved during a digital forensic investigation to prove or refute an alleged crime. Given resource constraints, it is not always feasible (or necessary) for law enforcement to retrieve all the related digital traces and to conduct comprehensive investigations. This paper attempts to address the issue by proposing a model for conducting swift, practical and cost-effective digital forensic investigations.
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.