Digital forensic practitioners are tasked with the identification, recovery, and analysis of Internet browser artefacts which may have been used in the pursuit of committing a civil or criminal offence. This research paper critically compares the most downloaded browser, Google Chrome, against an increasingly popular Chromium browser known as Brave, said to offer privacy-by-default. With increasing forensic caseloads, data complexity, and requirements for method validation to satisfy ISO 17025 accreditation, recognising the similarities and differences between the browsers, developed on the same underlying technology is essential. The paper describes a series of conducted experiments and subsequent analysis to identify artefacts created as part of normal user browsing activity. Analysis of the artefacts found that Brave and Chrome share almost identical data structures, with on-disk artefact recovery successful, even for deleted data. The outcome of this research, based upon the results, serves to enrich understanding and provide best practice for practitioners and software developers, respectively responsible for examining Chromium artefacts for use in evidence production and developing new forensic tools and techniques.
This study is a follow-up to Digital Evidence in Criminal Cases before the U.S. Courts of Appeal: Trends and Issues for Consideration - 2010 to 2015. The current study examines appeals of criminal cases before the United States Courts of Appeal from January 2016 through August 2020, where one or more appeal claims were related to digital evidence. The purpose of this research was to determine if the legal landscape has changed since 2015; examine the most relevant legal issues related to digital evidence; and analyze how precedential cases may have affected digital forensics as evidence.
The idiographic technical profiles of child sexual exploitation material (CSEM) offenders provide insight into their behaviours and context for their interactions with technology, but minimal quantitative work has been done to evaluate their sociability, technical ability and technophilia compared to non-offenders. This work used an online survey to compare an offender group consisting of English-speaking adults previously convicted of CSEM offenses (N = 78) with a reference population of non-offenders (N = 254). The survey assessed sociability, technical ability and technophilia through self-rating and information on occupation, level of education and device ownership. The study found that CSEM offenders had slightly lower sociability than non-offenders, though not at a level of clinical interest. Additionally, CSEM offenders had no statistically significant difference in technical ability and lower overall technophilia when compared to non-offenders. This study fails to support popular perceptions of CSEM offenders being technically savvy loners who are early adopters of new technologies.
This study was carried out intending to identify the level and type of information that remained on portable devices that were purchased from the second-hand market in the UK over the last few years. The sample for this study consisted of 100 second hand mobile phones and tablets. The aim of the study was to determine the proportion of devices that still contained data and the type of data that they contained. Where data was identified, the study attempted to determine the level of personal identifiable information that is associated with the previous owner. The research showed that when sensitive and personal data was present on a mobile device, in most of the cases there had been no attempt to remove it. However, fifty two percent of the mobile devices had been reset to the factory settings or had had all of the data erased, which demonstrates the previous owner’s attempt to permanently remove personal identifiable information. Twenty eight percent of the devices that were sold were not functional or recognized by the software used in the research. Twenty percent of the devices that contained data contained data that gave away the identity of the previous owner.
An Analysis of Product Liability for AI Entities with special reference to the Consumer Protection Act, 2019 Dr. Priya Roy & Mr. Rituraj Bhowal Abstract: In 1955 computer scientist John McCarthy initiated a research discipline known as Artificial Intelligence (AI). In the beginning of AI, it was indeed a matter of fascination as intelligent automated entities was in a captive state in human’s imagination. Surprisingly at present, that fascination has transformed into fear of many. Initially, when AI was being materialised, it was assigned for solving algebraic problems, playing games or speaking in distinctive languages. Till then AI was not in a state to create any disruption. However, the problem began in late 20th century and also in the beginning of 21st century, when AI was involved with more cognitive tasks it made experts to fear. From solving mathematics and playing games, humans put AI in charge of responsibilities traditionally performed by surgeon, judge, lawyers, police, armed forces and many more. With greater responsibility as assigned with AI, there arises much more liability. In order to deal with these new sets of liabilities for AI entities, scholars have suggested different methods either by way of applying coherentism approach or by deducing new set of rules. Application of the law of tort, specifically strict product liability on AI enabled products is one of such proposed methods. However, the distinctive attributes of AI, such as, unpredictability, inexplicability etc. makes application of product liability rules cumbersome. The present paper attempts to address in general the benefits and challenges behind the application of product liability rules in AI enabled entities. Further, the researcher will scrutinise the newly enacted legislation relating to product liability in India, i.e., the Consumer Protection Act, 2019 in the context of emergence of AI products. The said Act of 2019 recognises the product liability of three different categories of persons, viz., product manufacturer, product service provider and product seller. Each of these categories will be discussed in detail in order to find out whether Act of 2019 is well-equipped to deal with disruptions caused due to AI.
