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.
Research on using Bayesian networks to enhance digital forensic investigations has yet to evaluate the quality of the output of a Bayesian network. The evaluation can be performed by assessing the sensitivity of the posterior output of a forensic hypothesis to the input likelihood values of the digital evidence. This paper applies Bayesian sensitivity analysis techniques to a Bayesian network model for the well-known Yahoo! case. The analysis demonstrates that the conclusions drawn from Bayesian network models are statistically reliable and stable for small changes in evidence likelihood values.
The "Yahoo! Case" led to considerable debate about whether or not an IP address is personal data as defined by the Personal Data (Privacy) Ordinance (Chapter 486) of the Laws of Hong Kong. This paper discusses the digital evidence presented in the Yahoo! Case and evaluates the impact of the IP address on the verdict in the case. A Bayesian network is used to quantify the evidentiary strengths of hypotheses in the case and to reason about the evidence. The results demonstrate that the evidence about the IP address was significant to obtaining a conviction in the case.
Internet auction fraud has become prevalent. Methodologies for detecting fraudulent transactions use historical information about Internet auction participants to decide whether or not a user is a potential fraudster. The information includes reputation scores, values of items, time frames of various activities and transaction records. This paper presents a distinctive set of fraudster characteristics based on an analysis of 278 allegations about the sale of counterfeit goods at Internet auction sites. Also, it applies a Bayesian approach to analyze the relevance of evidence in Internet auction fraud cases.
This paper presents methods for analyzing the topology of a Bayesian belief network created to qualify and quantify the strengths of investigative hypotheses and their supporting digital evidence. The methods, which enable investigators to systematically establish, demonstrate and challenge a Bayesian belief network, help provide a powerful framework for reasoning about digital evidence. The methods are applied to review a Bayesian belief network constructed for a criminal case involving Bit-Torrent file sharing, and explain the causal effects underlying the legal arguments.
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