Users’ Privacy and Security Concerns that Affect IoT Adoption in the Home Domain

Schuster, Frederik; Habibipour, Abdolrasoul

doi:10.1080/10447318.2022.2147302

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…Acquisti et al 48 outlined the history of the development of the economic theory of privacy from its inception and advocated for restricting privacy in the name of an efficient market toward the counterarguments that continued to enhance privacy (i.e., restrictions on access to private information). Other researchers have looked at how privacy affects the adoption of technology (for instance, 49 or how the adoption of new technologies enhances privacy (Attié and Meyer-Waarden, 2022) or economic variables impacting privacy. 50 PETs offer greater security levels while being primarily created to gain anonymity.…”

Section: Literature Reviewmentioning

confidence: 99%

Privacy enhancing technology adoption and its impact on SMEs’ performance

Hasani

Rezania

Levallet

et al. 2023

International Journal of Engineering Business Management

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As society places greater emphasis on information privacy and data protection, organizations are increasingly adopting Privacy Enhancing Technologies (PETs) to safeguard the personal information of their stakeholders. This trend is fueled by growing consumer awareness and the introduction of government regulations aimed at protecting personal data. By implementing PETs, organizations can ensure compliance with privacy regulations and establish trust with their customers. This study aims to deepen the understanding of the determinants of Privacy Enhancing Technology (PET) adoption in small and medium-sized enterprises (SMEs) and its impact on their performance. It focuses on the technology-organization-environment (TOE) model, managerial readiness, firm size, industry sector, and intent to adopt PETs as potential drivers of PET adoption. By using a large-scale survey of 202 Canadian SMEs, the study evaluates the mediating role of intent in the relationship between the TOE model, managerial readiness, and market performance. The results of this study contribute to the growing body of research on PET adoption in SMEs and provide insights for organizations and managers to effectively adopt PETs. The results of this study indicate that technological, environmental, organizational, and managerial readiness have a positive effect on the intention to adopt PETs. Additionally, the intention to adopt PETs was found to have a positive relationship with firm performance. The findings also reveal that the intention to adopt PETs fully mediates the relationship between the four dimensions of readiness and firm performance. These findings highlight the important role that readiness and intention play in the adoption of PETs and its impact on firm performance. This study also found that firm size moderates the relationship between technological and organizational readiness with intention to adopt PETs, as well as the relationship between environmental and managerial readiness with intention to adopt PETs. The study identified the top five factors affecting PET adoption as cybersecurity awareness, perceived cost of adoption, ease of use, perceived benefits, and IT infrastructure. The findings suggest that technological readiness is the most influential of the four dimensions, followed by organizational, environmental, and managerial factors. This study presents crucial considerations for SMEs to evaluate when deciding on the use of PET technologies, as it pertains to practitioners.

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Section: Literature Reviewmentioning

confidence: 99%

Privacy enhancing technology adoption and its impact on SMEs’ performance

Hasani

Rezania

Levallet

et al. 2023

International Journal of Engineering Business Management

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“…In this regard, a wide range of research-based strategic solutions has been proposed in the past that could address the cyber-security loopholes and protect and verify the information flow within the network. The prime motive was laid on the  ISSN: 2088-8708 basis of protecting the information confidentiality and make the system sustainable and robust to different forms of threats and unauthorized access [5], [6]. This way, the concern rises gradually for IoT security.…”

Section: Introductionmentioning

confidence: 99%

An efficient security framework for intrusion detection and prevention in internet-of-things using machine learning technique

Nagaraj,

Channarayappa

2024

IJECE

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Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.

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“…This capacity enables them to assess the effectiveness of interventions and make informed decisions regarding program development. However, concerns related to the intrusive nature of such monitoring and the potential breach of private life have limited the adoption of such solutions [6].…”

Section: Introductionmentioning

confidence: 99%

A Smartphone-Based Architecture for Prolonged Monitoring of Gait

Bart,

Bechorfa,

Boutet

et al. 2024

2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)

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Gait analysis is important for evaluating neurological disorders such as stroke and Parkinson's disease. Traditionally, healthcare professionals had to rely on subjective assessments (i.e., human-based) of gait which were time consuming and not very reproducible. However, with the advent of IoT and indeed more objective (e.g., measurement-based) assessment methods, gait analysis can now be performed more accurately and effectively. It is worth noting, however, that there are still limitations to these objective methods, especially the lack of privacy-preserving continuous data collection. To overcome this limitation, we present in this paper a privacy-by-design monitoring application for post-stroke patients to evaluate their gait before, during, and after a rehabilitation program. Gait measurements are collected by a mobile application that continuously captures spatiotemporal parameters in the background using the built-in smartphone accelerometer. Statistical techniques are then applied to extract general indicators about the performed activity, as well as some more specific gait metrics in real-time such as regularity, symmetry and walking speed. These metrics are calculated based on the detected steps while patients are performing an activity. Additionally, a deep learning approach based on an auto-encoder is implemented to detect abnormal activities in the gait of patients. These analyses provides both valuable insights and statistical information about the activities performed by the patient, and a useful tool for practitioners to monitor the progression of neurological disorders and detect anomalies. We conducted experiments using this application in real conditions to monitor post-stroke patients in collaboration with a hospital, demonstrating its ability to compute valuable metrics and detect abnormal events patient's gait.

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