Forensic Investigation of Google Assistant

Akinbi, Alex; Berry, Thomas

doi:10.1007/s42979-020-00285-x

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…Cellebrite UFED cloud analyzer: The UFED cloud analyzer enables the acquisition and analysis of data from cloud accounts, including social media, email, and storage services [ 96 ]. It supports a wide range of cloud providers and helps in uncovering digital evidence.…”

Section: Cloud Digital Forensicsmentioning

confidence: 99%

Cloud Digital Forensics: Beyond Tools, Techniques, and Challenges

Malik,

Bhatti,

Park

et al. 2024

Sensors

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Cloud computing technology is rapidly becoming ubiquitous and indispensable. However, its widespread adoption also exposes organizations and individuals to a broad spectrum of potential threats. Despite the multiple advantages the cloud offers, organizations remain cautious about migrating their data and applications to the cloud due to fears of data breaches and security compromises. In light of these concerns, this study has conducted an in-depth examination of a variety of articles to enhance the comprehension of the challenges related to safeguarding and fortifying data within the cloud environment. Furthermore, the research has scrutinized several well-documented data breaches, analyzing the financial consequences they inflicted. Additionally, it scrutinizes the distinctions between conventional digital forensics and the forensic procedures specific to cloud computing. As a result of this investigation, the study has concluded by proposing potential opportunities for further research in this critical domain. By doing so, it contributes to our collective understanding of the complex panorama of cloud data protection and security, while acknowledging the evolving nature of technology and the need for ongoing exploration and innovation in this field. This study also helps in understanding the compound annual growth rate (CAGR) of cloud digital forensics, which is found to be quite high at ≈16.53% from 2023 to 2031. Moreover, its market is expected to reach ≈USD 36.9 billion by the year 2031; presently, it is ≈USD 11.21 billion, which shows that there are great opportunities for investment in this area. This study also strategically addresses emerging challenges in cloud digital forensics, providing a comprehensive approach to navigating and overcoming the complexities associated with the evolving landscape of cloud computing.

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Section: Cloud Digital Forensicsmentioning

confidence: 99%

Cloud Digital Forensics: Beyond Tools, Techniques, and Challenges

Malik,

Bhatti,

Park

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Due to the distributed nature and heterogeneity of IoT infrastructures as well as limitations of digital forensics tools, IoT forensics at the network and cloud levels have not been completely used in digital forensics and efforts to design standard frameworks, models, or methods for IoT forensics are still in their infancy [27]- [29]. However, there have been attempts to extract and analyze data from devices such as Google Home and Google Assistant apps [30].…”

Section: ) Internet Of Things (Iot) Forensicsmentioning

confidence: 99%

Cryptographic Techniques for Data Privacy in Digital Forensics

Ogunseyi,

Adedayo

2023

IEEE Access

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The acquisition and analysis of data in digital forensics raise different data privacy challenges. Many existing works on digital forensic readiness discuss what information should be stored and how to collect relevant data to facilitate investigations. However, the cost of this readiness often directly impacts the privacy of innocent third parties and suspects if the collected information is irrelevant. Approaches that have been suggested for privacy-preserving digital forensics focus on the use of policy, non-cryptography-based, and cryptography-based solutions. Cryptographic techniques have been proposed to address issues of data privacy during data analysis. As the utilization of some of these cryptographic techniques continues to increase, it is important to evaluate their applicability and challenges in relation to digital forensics processes. This study provides digital forensics investigators and researchers with a roadmap to understanding the data privacy challenges in digital forensics and examines the various privacy techniques that can be utilized to tackle these challenges. Specifically, we review the cryptographic techniques applied for privacy protection in digital forensics and categorize them within the context of whether they support trusted third parties, multiple investigators, and multi-keyword searches. We highlight some of the drawbacks of utilizing cryptography-based methods in privacy-preserving digital forensics and suggest potential solutions to the identified shortcomings. In addition, we propose a conceptual privacypreserving digital forensics (PPDF) model that is based on the use of cryptographic techniques and analyze the model within the context of the above-mentioned factors. An evaluation of the model is provided through a consideration of identified factors that may affect an investigation. Lastly, we provide an analysis of how existing principles for preserving privacy in digital forensics are addressed in our PPDF model. Our evaluation shows that the model aligns with many of the existing privacy principles recommended for privacy protection in digital forensics.

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“…The authors validated the performance quality of their proposed model by applying it to some case studies. The researchers in [247] were concentrated upon examining how to extract and analyze forensic artifacts from the Google Home and Google Assistant apps installed on an Android smartphone and how to apply them to controlling a Google Nest device (Google Home Mini smart speaker). They attempted to contribute to the body of knowledge in this field by exploring and analyzing the client-centric and cloudnative forensic artifacts.…”

Section: Network Forensicsmentioning

confidence: 99%

Digital Forensics Subdomains: The State of the Art and Future Directions

et al. 2021

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For reliable and relevant scientific evidence to be admitted in a court of law, it is important to apply digital forensic investigation techniques to corroborate a suspected potential security incident. Mainly, traditional digital forensics techniques have focused on computer desktops and servers. However, recent advances in digital media and platforms have seen an increased need for the application of digital forensic investigation techniques to other subdomains including small and mobile devices, databases, networks, cloudbased platforms, and the Internet of Things (IoT). To assist forensic investigators, conduct investigations within these subdomains, academic researchers have attempted to develop a number of investigative processes. However, many of these processes are domain-specific or describe domain-specific investigative tools. Hence, we hypothesize that the literature is littered with potentially overlapping and contradicting investigative process for conducting investigations within these subdomains. To investigate this hypothesis, a digital forensic model-orientated Systematic Literature Review (SLR) within the above digital forensic subdomains was undertaken. The purpose of the SLR was to identify the different and heterogeneous practices that have emerged within the specific subdomains. A key finding from the SLR is that there is a potential information overload and a high-degree of ambiguity among investigative processes in the above subdomains. The outcome of this study proposes a high-level abstract metamodel called The Digital Forensic Metamodel (DFM), which combines common processes, activities, techniques, and tasks for the above subdomains.

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Forensic Investigation of Google Assistant

Cited by 14 publications

References 15 publications

Cloud Digital Forensics: Beyond Tools, Techniques, and Challenges

Cloud Digital Forensics: Beyond Tools, Techniques, and Challenges

Cryptographic Techniques for Data Privacy in Digital Forensics

Digital Forensics Subdomains: The State of the Art and Future Directions

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