To See or Not to See: A Privacy Threat Model for Digital Forensics in Crime Investigation

2025-03-30

AI Summary

Digital forensics is critical in criminal investigations, yet evidence acquisition, processing, and storage frequently entail significant privacy risks; existing research lacks systematic threat identification and modeling, leading to regulatory noncompliance and rights violations. This paper introduces the SPADA methodology—previously unapplied in digital forensics—to establish the first privacy threat model that rigorously integrates legal compliance and ethical requirements. Through legal compliance analysis, cross-jurisdictional comparative assessment, and structured threat elicitation, we identify 298 domain-specific threats alongside several cross-cutting, generic threats. The resulting model enables threat classification, provenance tracing, and automated compliance evaluation. It has been operationalized in forensic tool design and policy development, thereby bridging a critical gap between privacy protection theory and judicial practice.

Technology Category

• Machine Learning: Privacy
• Philosophy and Ethics of AI: Privacy & Security
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Cyber-crime defenses and forensics
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices

Abstract

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.

Problem

• Identifies privacy threats in digital forensics investigations
• Applies SPADA methodology for privacy-oriented threat modeling
• Proposes model for ethical, legally compliant investigative practices

Innovation

• Applies SPADA for privacy threat modeling
• Identifies 298 domain-specific privacy threats
• Proposes universal privacy threat model