Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Jaberi, M.; Falk, T. H.
A Literature Survey on Potential Private User Information Leakage in Metaverse Applications Article de journal
Dans: Advanced Intelligent Systems, vol. 8, no 1, 2026, ISSN: 26404567 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: age, augmented reality, Brain, brain computer interface, Computer privacy, Data privacy, Electroencephalography, eye tracking, Gender, Haptic interfaces, Information leakage, Intelligent systems, Literature survey, metaverse, Metaverses, Neurophysiological signal, neurophysiological signals, Neurophysiology, Privacy, Private user information, Race, Signal analysis, User information, Virtual and augmented reality
@article{jaberiLiteratureSurveyPotential2026,
title = {A Literature Survey on Potential Private User Information Leakage in Metaverse Applications},
author = {M. Jaberi and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105012929167?origin=resultslist},
doi = {10.1002/aisy.202500263},
issn = {26404567 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Advanced Intelligent Systems},
volume = {8},
number = {1},
publisher = {John Wiley and Sons Inc},
abstract = {The Metaverse is revolutionizing various fields, including healthcare, education, social interaction, and the workplace. Commercial multisensory devices (e.g., smell diffusion and haptic technologies) are available, and virtual and augmented reality (VR/AR) headsets are increasingly integrated with brain–computer interfaces (BCI). These integrations enable adaptive, personalized virtual immersive experiences that are more engaging, interactive, and effective. As these applications become mainstream, concerns arise regarding the security and privacy of personal information. Recent studies demonstrate that users can be identified with high accuracy using the data monitored from sensors available in VR/AR headsets. This literature survey investigates the types of personal user information that can be inferred from BCI-instrumented headsets. In particular, it focuses on predicting age, gender, and ethnic/racial background from neurophysiological signals currently monitored by commercial devices. The survey highlights the predictive strength of electroencephalogram and electrocardiogram signal modalities, followed by eye tracking and iris scanning. It also considers future privacy risks posed by biometric and gesture-based monitoring using non-contact technologies such as computer vision and WiFi signal analysis. The survey concludes with recommendations for future research aimed at contributing to the development of robust frameworks that safeguard user privacy in the evolving Metaverse landscape. © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.},
keywords = {age, augmented reality, Brain, brain computer interface, Computer privacy, Data privacy, Electroencephalography, eye tracking, Gender, Haptic interfaces, Information leakage, Intelligent systems, Literature survey, metaverse, Metaverses, Neurophysiological signal, neurophysiological signals, Neurophysiology, Privacy, Private user information, Race, Signal analysis, User information, Virtual and augmented reality},
pubstate = {published},
tppubtype = {article}
}
The Metaverse is revolutionizing various fields, including healthcare, education, social interaction, and the workplace. Commercial multisensory devices (e.g., smell diffusion and haptic technologies) are available, and virtual and augmented reality (VR/AR) headsets are increasingly integrated with brain–computer interfaces (BCI). These integrations enable adaptive, personalized virtual immersive experiences that are more engaging, interactive, and effective. As these applications become mainstream, concerns arise regarding the security and privacy of personal information. Recent studies demonstrate that users can be identified with high accuracy using the data monitored from sensors available in VR/AR headsets. This literature survey investigates the types of personal user information that can be inferred from BCI-instrumented headsets. In particular, it focuses on predicting age, gender, and ethnic/racial background from neurophysiological signals currently monitored by commercial devices. The survey highlights the predictive strength of electroencephalogram and electrocardiogram signal modalities, followed by eye tracking and iris scanning. It also considers future privacy risks posed by biometric and gesture-based monitoring using non-contact technologies such as computer vision and WiFi signal analysis. The survey concludes with recommendations for future research aimed at contributing to the development of robust frameworks that safeguard user privacy in the evolving Metaverse landscape. © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.
2.
Dang, L. B.; Truong, V. Tuan; Le, L. B.; Falk, T. H.
EM-Auth: A Generalizable Eye-Movement Authentication Framework using Event Cameras Article d'actes
Dans: IEEE Conf. Artif. Intell., CAI, p. 2064–2069, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833156039-3 (ISBN), (Journal Abbreviation: IEEE Conf. Artif. Intell., CAI).
Résumé | Liens | BibTeX | Étiquettes: augmented reality, Authentication, Cameras, Convolutional neural networks, Data privacy, Eye movement patterns, Eye movements, eye tracking, Eye-tracking, Frame-based, High power consumption, Neuromorphic vision sensors, Power potential, Privacy concerns, Temporal resolution, Transfer learning, User authentication, Virtual and augmented reality, virtual reality
@inproceedings{dangEMAuthGeneralizableEyeMovement2026,
title = {EM-Auth: A Generalizable Eye-Movement Authentication Framework using Event Cameras},
author = {L. B. Dang and V. Tuan Truong and L. B. Le and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105042114237?origin=resultslist},
doi = {10.1109/CAI68641.2026.11536355},
isbn = {979-833156039-3 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Conf. Artif. Intell., CAI},
pages = {2064–2069},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {With the rapid emergence of virtual and augmented reality (VR/AR) head-mounted devices that closely interact with users' eyes, eye-tracking-based applications are becoming increasingly promising. Among them, eye movement patterns have emerged as a reliable biometric modality for user authentication. However, existing solutions based on conventional frame-based cameras face significant limitations, including low temporal resolution, high latency, high power consumption, and potential privacy concerns due to the detailed appearance captured around the eyes. To address these challenges, we propose EM-Auth, a novel framework for eye-tracking-based user authentication that leverages event cameras, a type of neuromorphic vision sensor providing asynchronous, high-temporal-resolution data, while preserving user privacy. Our method introduces a two-stage spatiotemporal feature extraction pipeline: first, a lightweight convolutional neural network (CNN) extracts spatial features from event streams; second, a transformer-based module models the temporal dependencies to generate robust eye representations. Extensive experiments show that our framework significantly outperforms existing state-of-the-art (SOTA) methods, achieving 0.008 and 0.015 equal error rates (EERs) on two representative datasets. Notably, EM-Auth demonstrates a strong generalization capability. In a zero-shot setting where the model is trained on a specific dataset and evaluated on previously unseen users, our approach achieves authentication accuracies of 98.4% and 95.9% on the EV-Eye and EBV-Eye datasets, respectively. Under cross-dataset evaluation, our framework achieves an authentication accuracy of nearly 85% without additional fine-tuning, and consistently improves performance through transfer learning. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},
keywords = {augmented reality, Authentication, Cameras, Convolutional neural networks, Data privacy, Eye movement patterns, Eye movements, eye tracking, Eye-tracking, Frame-based, High power consumption, Neuromorphic vision sensors, Power potential, Privacy concerns, Temporal resolution, Transfer learning, User authentication, Virtual and augmented reality, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
With the rapid emergence of virtual and augmented reality (VR/AR) head-mounted devices that closely interact with users' eyes, eye-tracking-based applications are becoming increasingly promising. Among them, eye movement patterns have emerged as a reliable biometric modality for user authentication. However, existing solutions based on conventional frame-based cameras face significant limitations, including low temporal resolution, high latency, high power consumption, and potential privacy concerns due to the detailed appearance captured around the eyes. To address these challenges, we propose EM-Auth, a novel framework for eye-tracking-based user authentication that leverages event cameras, a type of neuromorphic vision sensor providing asynchronous, high-temporal-resolution data, while preserving user privacy. Our method introduces a two-stage spatiotemporal feature extraction pipeline: first, a lightweight convolutional neural network (CNN) extracts spatial features from event streams; second, a transformer-based module models the temporal dependencies to generate robust eye representations. Extensive experiments show that our framework significantly outperforms existing state-of-the-art (SOTA) methods, achieving 0.008 and 0.015 equal error rates (EERs) on two representative datasets. Notably, EM-Auth demonstrates a strong generalization capability. In a zero-shot setting where the model is trained on a specific dataset and evaluated on previously unseen users, our approach achieves authentication accuracies of 98.4% and 95.9% on the EV-Eye and EBV-Eye datasets, respectively. Under cross-dataset evaluation, our framework achieves an authentication accuracy of nearly 85% without additional fine-tuning, and consistently improves performance through transfer learning. © 2026 IEEE.
3.
Cheddadi, A. El; Moudoud, H.; Gagnon, S.
The role of artificial intelligence and machine learning in safeguarding IoMT security and privacy Article de journal
Dans: Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability, p. 1–17, 2025, ISSN: 978-100364032-5 (ISBN); 978-104107046-7 (ISBN).
Liens | BibTeX | Étiquettes: Artificial intelligence learning, Data privacy, Learning systems, Machine learning, Machine-learning, Security and privacy
@article{el_cheddadi_role_2025,
title = {The role of artificial intelligence and machine learning in safeguarding IoMT security and privacy},
author = {A. El Cheddadi and H. Moudoud and S. Gagnon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024398178&doi=10.1201%2F9781003640325-1&partnerID=40&md5=442f29d04b42d63f9fd11c83ad4a77df},
doi = {10.1201/9781003640325-1},
issn = {978-100364032-5 (ISBN); 978-104107046-7 (ISBN)},
year = {2025},
date = {2025-01-01},
journal = {Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability},
pages = {1–17},
keywords = {Artificial intelligence learning, Data privacy, Learning systems, Machine learning, Machine-learning, Security and privacy},
pubstate = {published},
tppubtype = {article}
}
4.
Amari, H.; Houda, Z. A. El; Moudoud, H.; Khoukhi, L.; Belguith, L. H.
Blockchain-Based Federated Learning for Enhanced Cyber-Threats Detection in Connected Vehicles Article d'actes
Dans: M., Valenti; D., Reed; M., Torres (Ed.): IEEE Int Conf Commun, p. 4257–4262, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 15503607 (ISSN); 979-833150521-9 (ISBN), (Journal Abbreviation: IEEE Int Conf Commun).
Résumé | Liens | BibTeX | Étiquettes: Block-chain, Blockchain, Central layers, Computer crime, Connected vehicle, Connected Vehicles, Cyber threats, Cyberthreat detection, Cyberthreats, Cyberthreats Detection, Data privacy, Federated learning, Intelligent transport, Intelligent vehicle highway systems, Internet of things, Intrusion Detection, Intrusion Detection Systems, Learning systems, Network security, SDN, Threat detection, Traffic control, Vehicles
@inproceedings{amariBlockchainBasedFederatedLearning2025,
title = {Blockchain-Based Federated Learning for Enhanced Cyber-Threats Detection in Connected Vehicles},
author = {H. Amari and Z. A. El Houda and H. Moudoud and L. Khoukhi and L. H. Belguith},
editor = {Valenti M. and Reed D. and Torres M.},
url = {https://www.scopus.com/pages/publications/105018456633?origin=resultslist},
doi = {10.1109/ICC52391.2025.11161266},
isbn = {15503607 (ISSN); 979-833150521-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IEEE Int Conf Commun},
pages = {4257–4262},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Over the past few years, there have been made significant strides in advancing the Internet of Vehicles (IoV), recognizing its strategic importance in Intelligent Transport Systems. The proliferation of connected and autonomous vehicles on the roads has propelled the IoV into the spotlight. However, addressing the specific demands of vehicular networks, such as low latency, high mobility, extensive connectivity of 5G/6G networks, and robust security, remains a substantial challenge. Therefore, there is a critical need for substantial progress in implementing a resilient Intrusion Detection System within the IoV ecosystem. This paper introduces VFed-IDS, a decentralized, secure, flexible, scalable, and robust Blockchain and Federated Learning-based intrusion detection system. VFed-IDS is designed to identify cyber threats in the IoV while preserving privacy in connected vehicles. The proposed architecture consists of three main layers: the central layer, the local layer, and the Blockchain layer. The central layer includes the SDN Controller, responsible for training and aggregating the global model. The local layer comprises vehicles training individual models based on their private local datasets. The Blockchain layer introduces the Smart Contract VFed-SC, which manages the list of authenticated and collaborating vehicles in the Federated Learning process. It also hashes trained local model updates before transmitting them as transactions between the central and local layers. Simulation results demonstrate that VFed-IDS achieves a high accuracy rate of 99%, effectively enhancing the autonomous behavior of connected vehicles against cyber threats. © 2025 IEEE.},
note = {Journal Abbreviation: IEEE Int Conf Commun},
keywords = {Block-chain, Blockchain, Central layers, Computer crime, Connected vehicle, Connected Vehicles, Cyber threats, Cyberthreat detection, Cyberthreats, Cyberthreats Detection, Data privacy, Federated learning, Intelligent transport, Intelligent vehicle highway systems, Internet of things, Intrusion Detection, Intrusion Detection Systems, Learning systems, Network security, SDN, Threat detection, Traffic control, Vehicles},
pubstate = {published},
tppubtype = {inproceedings}
}
Over the past few years, there have been made significant strides in advancing the Internet of Vehicles (IoV), recognizing its strategic importance in Intelligent Transport Systems. The proliferation of connected and autonomous vehicles on the roads has propelled the IoV into the spotlight. However, addressing the specific demands of vehicular networks, such as low latency, high mobility, extensive connectivity of 5G/6G networks, and robust security, remains a substantial challenge. Therefore, there is a critical need for substantial progress in implementing a resilient Intrusion Detection System within the IoV ecosystem. This paper introduces VFed-IDS, a decentralized, secure, flexible, scalable, and robust Blockchain and Federated Learning-based intrusion detection system. VFed-IDS is designed to identify cyber threats in the IoV while preserving privacy in connected vehicles. The proposed architecture consists of three main layers: the central layer, the local layer, and the Blockchain layer. The central layer includes the SDN Controller, responsible for training and aggregating the global model. The local layer comprises vehicles training individual models based on their private local datasets. The Blockchain layer introduces the Smart Contract VFed-SC, which manages the list of authenticated and collaborating vehicles in the Federated Learning process. It also hashes trained local model updates before transmitting them as transactions between the central and local layers. Simulation results demonstrate that VFed-IDS achieves a high accuracy rate of 99%, effectively enhancing the autonomous behavior of connected vehicles against cyber threats. © 2025 IEEE.
5.
Cheddadi, A. El; Moudoud, H.; Gagnon, S.
The role of artificial intelligence and machine learning in safeguarding IoMT security and privacy Article de journal
Dans: Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability, p. 1–17, 2025, ISSN: 978-100364032-5 (ISBN); 978-104107046-7 (ISBN).
Liens | BibTeX | Étiquettes: Artificial intelligence learning, Data privacy, Learning systems, Machine learning, Machine-learning, Security and privacy
@article{elcheddadiRoleArtificialIntelligence2025,
title = {The role of artificial intelligence and machine learning in safeguarding IoMT security and privacy},
author = {A. El Cheddadi and H. Moudoud and S. Gagnon},
url = {https://www.scopus.com/pages/publications/105024398178?origin=resultslist},
doi = {10.1201/9781003640325-1},
issn = {978-100364032-5 (ISBN); 978-104107046-7 (ISBN)},
year = {2025},
date = {2025-01-01},
journal = {Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability},
pages = {1–17},
publisher = {CRC Press},
keywords = {Artificial intelligence learning, Data privacy, Learning systems, Machine learning, Machine-learning, Security and privacy},
pubstate = {published},
tppubtype = {article}
}
6.
Mehrban, A.; Houda, Z. A. El; Moudoud, H.; Brik, B.; Khoukhi, L.
A Blockchain-Enabled Multi-Layered Zero-Trust Security Framework for O-RAN Article d'actes
Dans: Int. Wirel. Commun. Mob. Comput. Conf., IWCMC, p. 1564–1569, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833150887-6 (ISBN), (Journal Abbreviation: Int. Wirel. Commun. Mob. Comput. Conf., IWCMC).
Résumé | Liens | BibTeX | Étiquettes: Access control, Block-chain, Blockchain, Data privacy, Federated learning, federated learning (FL), Internet of thing, Internet of things, Internet of Things (IoT), Interoperability, Learning systems, Mobile telecommunication systems, Multi-layered, Network architecture, Network security, Open access, Open radio access network, open radio access network (O-RAN), Radio, Radio access networks, Radio access technologies, Radio communication, Secure communication, Security, Security frameworks, Security risks, Transfer learning, Trusted computing
@inproceedings{mehrbanBlockchainEnabledMultiLayeredZeroTrust2025,
title = {A Blockchain-Enabled Multi-Layered Zero-Trust Security Framework for O-RAN},
author = {A. Mehrban and Z. A. El Houda and H. Moudoud and B. Brik and L. Khoukhi},
url = {https://www.scopus.com/pages/publications/105011345211?origin=resultslist},
doi = {10.1109/IWCMC65282.2025.11059720},
isbn = {979-833150887-6 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int. Wirel. Commun. Mob. Comput. Conf., IWCMC},
pages = {1564–1569},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {O-RAN (Open Radio Access Network) is a set of open and interoperable radio access technologies, guided by the O-RAN Alliance, that, despite an open ecosystem, introduces significant security risks, expanding the threat surface in 6G networks. Traditional perimeter-based security approaches are inadequate for O-RAN's highly distributed, multi-vendor environments, where Zero Trust Architecture (ZTA) becomes essential for robust security. To address these challenges, we propose a novel blockchain-based, decentralized Zero-Trust Framework specifically designed for O-RAN security. Our proposed framework comprises two key layers: the first layer utilizes Federated Learning (FL) and Transfer Learning (TL) for advanced attack detection, enabling distributed, privacy-preserving threat analysis across O-RAN nodes. The second layer enforces Zero Trust access control through a blockchain-based identity management system, ensuring tamper-resistant, real-time policy updates. This multi-layered framework provides adaptive threat detection and resilient access control, validated through simulations demonstrating high detection accuracy and robust access management with minimal impact on network performance, offering a scalable security solution for next-generation O-RAN deployments. © 2025 IEEE.},
note = {Journal Abbreviation: Int. Wirel. Commun. Mob. Comput. Conf., IWCMC},
keywords = {Access control, Block-chain, Blockchain, Data privacy, Federated learning, federated learning (FL), Internet of thing, Internet of things, Internet of Things (IoT), Interoperability, Learning systems, Mobile telecommunication systems, Multi-layered, Network architecture, Network security, Open access, Open radio access network, open radio access network (O-RAN), Radio, Radio access networks, Radio access technologies, Radio communication, Secure communication, Security, Security frameworks, Security risks, Transfer learning, Trusted computing},
pubstate = {published},
tppubtype = {inproceedings}
}
O-RAN (Open Radio Access Network) is a set of open and interoperable radio access technologies, guided by the O-RAN Alliance, that, despite an open ecosystem, introduces significant security risks, expanding the threat surface in 6G networks. Traditional perimeter-based security approaches are inadequate for O-RAN's highly distributed, multi-vendor environments, where Zero Trust Architecture (ZTA) becomes essential for robust security. To address these challenges, we propose a novel blockchain-based, decentralized Zero-Trust Framework specifically designed for O-RAN security. Our proposed framework comprises two key layers: the first layer utilizes Federated Learning (FL) and Transfer Learning (TL) for advanced attack detection, enabling distributed, privacy-preserving threat analysis across O-RAN nodes. The second layer enforces Zero Trust access control through a blockchain-based identity management system, ensuring tamper-resistant, real-time policy updates. This multi-layered framework provides adaptive threat detection and resilient access control, validated through simulations demonstrating high detection accuracy and robust access management with minimal impact on network performance, offering a scalable security solution for next-generation O-RAN deployments. © 2025 IEEE.
7.
Cheddadi, A. El; Moudoud, H.; Gagnon, S.
The role of artificial intelligence and machine learning in safeguarding IoMT security and privacy Article de journal
Dans: Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability, p. 1–17, 2025, ISSN: 978-100364032-5 (ISBN); 978-104107046-7 (ISBN).
Liens | BibTeX | Étiquettes: Artificial intelligence learning, Data privacy, Learning systems, Machine learning, Machine-learning, Security and privacy
@article{elcheddadiRoleArtificialIntelligence2025a,
title = {The role of artificial intelligence and machine learning in safeguarding IoMT security and privacy},
author = {A. El Cheddadi and H. Moudoud and S. Gagnon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024398178&doi=10.1201%2F9781003640325-1&partnerID=40&md5=442f29d04b42d63f9fd11c83ad4a77df},
doi = {10.1201/9781003640325-1},
issn = {978-100364032-5 (ISBN); 978-104107046-7 (ISBN)},
year = {2025},
date = {2025-01-01},
journal = {Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability},
pages = {1–17},
keywords = {Artificial intelligence learning, Data privacy, Learning systems, Machine learning, Machine-learning, Security and privacy},
pubstate = {published},
tppubtype = {article}
}
8.
Margulis, A.; Boeck, H.; Bendavid, Y.; Durif, F.
Building theory from consumer reactions to RFID: discovering Connective Proximity Article de journal
Dans: Ethics and Information Technology, vol. 18, no 2, p. 81–101, 2016, ISSN: 13881957 (ISSN), (Publisher: Springer Netherlands).
Résumé | Liens | BibTeX | Étiquettes: case study, Consumer, Data privacy, Ethical considerations, Ethics, Human Tracking, Marketing activities, Nature of the technologies, Philosophical aspects, Physical proximity, Privacy, Radio frequency identification (RFID), RFID, Sales representatives
@article{margulis_building_2016,
title = {Building theory from consumer reactions to RFID: discovering Connective Proximity},
author = {A. Margulis and H. Boeck and Y. Bendavid and F. Durif},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959508921&doi=10.1007%2fs10676-016-9388-y&partnerID=40&md5=ed3ae225f1719b39f9b65321567151ff},
doi = {10.1007/s10676-016-9388-y},
issn = {13881957 (ISSN)},
year = {2016},
date = {2016-01-01},
journal = {Ethics and Information Technology},
volume = {18},
number = {2},
pages = {81–101},
publisher = {Springer Netherlands},
abstract = {Academic research into RFID technology has largely neglected ethics. What scarce research was being performed has now dwindled despite having some consumers continue to express their ethical concerns. This article aims at reducing this apparent void by exploring the antecedents that cause the public to react when consumers are targeted by RFID technology. Through the analysis of 11 real RFID implementations used to target consumers, our research indicates that several factors can influence consumer response through the distinct nature of the technology, namely, (1) Customization of communications, (2) Imposition of tag use, (3) Segmentation and targeting, (4) Modified role of the sales representative, (5) Physical distance between the consumer and the tag, and (6) Security of transactions. The article then proposes a construct to determine the risk of raising consumer ethical concerns. The construct of “Connective Proximity” and its three components (physical proximity, exposure time, and information proximity) are defined. Our research highlights the need for further studies on the ethical considerations of tagging humans and specifically tracking consumers when performing marketing activities with RFID technology. Our article aims at invigorating research on this topic, which has a lot to contribute to both society and corporations. © 2016, Springer Science+Business Media Dordrecht.},
note = {Publisher: Springer Netherlands},
keywords = {case study, Consumer, Data privacy, Ethical considerations, Ethics, Human Tracking, Marketing activities, Nature of the technologies, Philosophical aspects, Physical proximity, Privacy, Radio frequency identification (RFID), RFID, Sales representatives},
pubstate = {published},
tppubtype = {article}
}
Academic research into RFID technology has largely neglected ethics. What scarce research was being performed has now dwindled despite having some consumers continue to express their ethical concerns. This article aims at reducing this apparent void by exploring the antecedents that cause the public to react when consumers are targeted by RFID technology. Through the analysis of 11 real RFID implementations used to target consumers, our research indicates that several factors can influence consumer response through the distinct nature of the technology, namely, (1) Customization of communications, (2) Imposition of tag use, (3) Segmentation and targeting, (4) Modified role of the sales representative, (5) Physical distance between the consumer and the tag, and (6) Security of transactions. The article then proposes a construct to determine the risk of raising consumer ethical concerns. The construct of “Connective Proximity” and its three components (physical proximity, exposure time, and information proximity) are defined. Our research highlights the need for further studies on the ethical considerations of tagging humans and specifically tracking consumers when performing marketing activities with RFID technology. Our article aims at invigorating research on this topic, which has a lot to contribute to both society and corporations. © 2016, Springer Science+Business Media Dordrecht.
9.
Margulis, A.; Boeck, H.; Bendavid, Y.; Durif, F.
Building theory from consumer reactions to RFID: discovering Connective Proximity Article de journal
Dans: Ethics and Information Technology, vol. 18, no 2, p. 81–101, 2016, ISSN: 13881957 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: case study, Consumer, Data privacy, Ethical considerations, Ethics, Human Tracking, Marketing activities, Nature of the technologies, Philosophical aspects, Physical proximity, Privacy, Radio frequency identification (RFID), RFID, Sales representatives
@article{margulisBuildingTheoryConsumer2016,
title = {Building theory from consumer reactions to RFID: discovering Connective Proximity},
author = {A. Margulis and H. Boeck and Y. Bendavid and F. Durif},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959508921&doi=10.1007%2fs10676-016-9388-y&partnerID=40&md5=ed3ae225f1719b39f9b65321567151ff},
doi = {10.1007/s10676-016-9388-y},
issn = {13881957 (ISSN)},
year = {2016},
date = {2016-01-01},
journal = {Ethics and Information Technology},
volume = {18},
number = {2},
pages = {81–101},
publisher = {Springer Netherlands},
abstract = {Academic research into RFID technology has largely neglected ethics. What scarce research was being performed has now dwindled despite having some consumers continue to express their ethical concerns. This article aims at reducing this apparent void by exploring the antecedents that cause the public to react when consumers are targeted by RFID technology. Through the analysis of 11 real RFID implementations used to target consumers, our research indicates that several factors can influence consumer response through the distinct nature of the technology, namely, (1) Customization of communications, (2) Imposition of tag use, (3) Segmentation and targeting, (4) Modified role of the sales representative, (5) Physical distance between the consumer and the tag, and (6) Security of transactions. The article then proposes a construct to determine the risk of raising consumer ethical concerns. The construct of “Connective Proximity” and its three components (physical proximity, exposure time, and information proximity) are defined. Our research highlights the need for further studies on the ethical considerations of tagging humans and specifically tracking consumers when performing marketing activities with RFID technology. Our article aims at invigorating research on this topic, which has a lot to contribute to both society and corporations. © 2016, Springer Science+Business Media Dordrecht.},
keywords = {case study, Consumer, Data privacy, Ethical considerations, Ethics, Human Tracking, Marketing activities, Nature of the technologies, Philosophical aspects, Physical proximity, Privacy, Radio frequency identification (RFID), RFID, Sales representatives},
pubstate = {published},
tppubtype = {article}
}
Academic research into RFID technology has largely neglected ethics. What scarce research was being performed has now dwindled despite having some consumers continue to express their ethical concerns. This article aims at reducing this apparent void by exploring the antecedents that cause the public to react when consumers are targeted by RFID technology. Through the analysis of 11 real RFID implementations used to target consumers, our research indicates that several factors can influence consumer response through the distinct nature of the technology, namely, (1) Customization of communications, (2) Imposition of tag use, (3) Segmentation and targeting, (4) Modified role of the sales representative, (5) Physical distance between the consumer and the tag, and (6) Security of transactions. The article then proposes a construct to determine the risk of raising consumer ethical concerns. The construct of “Connective Proximity” and its three components (physical proximity, exposure time, and information proximity) are defined. Our research highlights the need for further studies on the ethical considerations of tagging humans and specifically tracking consumers when performing marketing activities with RFID technology. Our article aims at invigorating research on this topic, which has a lot to contribute to both society and corporations. © 2016, Springer Science+Business Media Dordrecht.