Mario Raciti

Digital forensics is a cornerstone of modern crime investigations, yet it raises significant privacy concerns due to the collection, processing, and storage of digital evidence. Despite that, privacy threats in digital forensics crime investigations often remain underexplored, thereby leading to potential gaps in forensic practices and regulatory compliance, which may then escalate into harming the freedoms of natural persons. With this clear motivation, the present paper applies the SPADA methodology for threat modelling with the goal of incorporating privacy-oriented threat modelling in digital forensics. As a result, we identify a total of 298 privacy threats that may affect digital forensics processes through crime investigations. Furthermore, we demonstrate an unexplored feature on how SPADA assists in handling domain-dependency during threat elicitation. This yields a second list of privacy threats that are universally applicable to any domain. We then present a comprehensive and systematic privacy threat model for digital forensics in crime investigation. Moreover, we discuss some of the challenges about validating privacy threats in this domain, particularly given the variability of legal frameworks across jurisdictions. We ultimately propose our privacy threat model as a tool for ensuring ethical and legally compliant investigative practices.

Digital forensics is a cornerstone of modern crime investigations, yet it raises significant privacy concerns due to the collection, processing, and storage of digital evidence. Despite that, privacy threats in digital forensics crime investigations often remain underexplored, thereby leading to potential gaps in forensic practices and regulatory compliance, which may then escalate into harming the freedoms of natural persons. With this clear motivation, the present paper applies the SPADA methodology for threat modelling with the goal of incorporating privacy-oriented threat modelling in digital forensics. As a result, we identify a total of 298 privacy threats that may affect digital forensics processes through crime investigations. Furthermore, we demonstrate an unexplored feature on how SPADA assists in handling domain-dependency during threat elicitation. This yields a second list of privacy threats that are universally applicable to any domain. We then present a comprehensive and systematic privacy threat model for digital forensics in crime investigation. Moreover, we discuss some of the challenges about validating privacy threats in this domain, particularly given the variability of legal frameworks across jurisdictions. We ultimately propose our privacy threat model as a tool for ensuring ethical and legally compliant investigative practices.

As individuals engage with innovative technologies, including smart cars and smart homes, a comprehensive treatment of the threats to their privacy becomes increasingly urgent. This article recognises the relevance of security and, in particular, privacy threat modelling, especially under the umbrella of GDPR compliance, and addresses the challenge of the pursuit of completeness in eliciting security and privacy threats. The core contribution is SPADA, a methodology for threat modelling revolving around five key variables (whose initials form the acronym that names the methodology). These are: “Source of documentation”, “Property”, “Application domain”, “Detail (level of)” and “Agent(s) raising the threats”, and clarify the essential variable elements of the threat modelling activity. SPADA requires the analyst to duly instantiate each variable but offers increased structure and automation in return. The methodology is applied to the domains of smart cars and smart homes, considering both soft and hard privacy. This yields 23 domain-independent threats for soft privacy, and 29 domain-independent threats for hard privacy. Both these lists of threats are then tailored to the smart car domain by appropriate combination with the 43 identified assets, producing a total of 785 privacy threats for smart cars. Similarly, appropriate combination with the 127 assets identified in the smart home domain produces a total of 1502 privacy threats for smart homes.

Within the dynamic realm of Intelligent Transportation Systems (ITS), the Internet of Vehicles (IoV) marks a significant paradigm shift. IoV is an interconnected network of vehicles, infrastructures, and the Internet, driven by wireless communication technologies. This paper dissects the privacy landscapes of ITS and IoV, exploring gaps and redundancies in standards and academic literature. We do so by leveraging European Telecommunications Standards Institute (ETSI) ITS G5 standards and IoV analyses from literature, and building two relational models to depict said privacy landscapes. A macroscopic analysis reveals structural and thematic differences: ITS, governed by established standards, has a robust structure, while IoV, in its nascent stage, lacks formalisation. A detailed analysis highlights challenges in data collection, sharing, and privacy policies. As ITS transitions to IoV, increasing data volume demands enhanced privacy safeguards. Addressing these challenges requires collaborative efforts to develop comprehensive privacy policies, prioritise user awareness, and integrate privacy-by-design principles. This paper offers insights into navigating the evolving landscape of transportation technologies, laying the groundwork for privacy-preserving ITS and IoV ecosystems.