Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Elhajjout, A.; Houda, Z. A. E.; Moudoud, H.; Brik, B.; Jan, M. A.
Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions Journal Article
In: IEEE Journal of Biomedical and Health Informatics, 2026, ISSN: 21682194 (ISSN).
Abstract | Links | BibTeX | Tags: Application research, Artificial intelligence, digital health, Distributed computer systems, Federated learning, Fine tuning, Health care, Health care application, Healthcare AI, human, Internet, Internet of medical thing, Internet of Medical Things, Language model, Large language model, large language models, Learning systems, male, Medical computing, natural language processing, Parameter-efficient fine-tuning, patient coding, Privacy, Privacy preservation, Privacy preserving, Privacy-preserving techniques, review, Sensitive data, trustworthiness
@article{elhajjoutFederatedLargeLanguage2026,
title = {Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions},
author = {A. Elhajjout and Z. A. E. Houda and H. Moudoud and B. Brik and M. A. Jan},
url = {https://www.scopus.com/pages/publications/105034640126?origin=resultslist},
doi = {10.1109/JBHI.2026.3679612},
issn = {21682194 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Large-scale foundation models, especially Federated Large Language Models (FLLMs), aim to transform digital health by enabling clinically-grade natural language processing while keeping sensitive data local. However, their adoption is hindered by two main issues: (i) the computational and communication burden of parameter-rich models on resource-constrained Internet-of-Medical-Things (IoMT) devices, and (ii) performance degradation caused by Non-Independent and Identically Distributed (Non-IID) patient data. This paper presents a comprehensive survey of Federated Learning (FL) for LLMs in Healthcare (FedMed-LLMs). We review the foundations of FL and medical LLMs. Then, we present the FL-enabled LLMs applications in healthcare, and we examine their issues in terms of privacy, robustness, and trustworthiness. Finally, we present a set of core research problems and a comprehensive research agenda that identifies future directions for building robust and scalable FedMed-LLMs systems. © 2013 IEEE.},
keywords = {Application research, Artificial intelligence, digital health, Distributed computer systems, Federated learning, Fine tuning, Health care, Health care application, Healthcare AI, human, Internet, Internet of medical thing, Internet of Medical Things, Language model, Large language model, large language models, Learning systems, male, Medical computing, natural language processing, Parameter-efficient fine-tuning, patient coding, Privacy, Privacy preservation, Privacy preserving, Privacy-preserving techniques, review, Sensitive data, trustworthiness},
pubstate = {published},
tppubtype = {article}
}
Large-scale foundation models, especially Federated Large Language Models (FLLMs), aim to transform digital health by enabling clinically-grade natural language processing while keeping sensitive data local. However, their adoption is hindered by two main issues: (i) the computational and communication burden of parameter-rich models on resource-constrained Internet-of-Medical-Things (IoMT) devices, and (ii) performance degradation caused by Non-Independent and Identically Distributed (Non-IID) patient data. This paper presents a comprehensive survey of Federated Learning (FL) for LLMs in Healthcare (FedMed-LLMs). We review the foundations of FL and medical LLMs. Then, we present the FL-enabled LLMs applications in healthcare, and we examine their issues in terms of privacy, robustness, and trustworthiness. Finally, we present a set of core research problems and a comprehensive research agenda that identifies future directions for building robust and scalable FedMed-LLMs systems. © 2013 IEEE.
2.
Jaberi, M.; Falk, T. H.
A Literature Survey on Potential Private User Information Leakage in Metaverse Applications Journal Article
In: Advanced Intelligent Systems, vol. 8, no. 1, 2026, ISSN: 26404567 (ISSN).
Abstract | Links | BibTeX | Tags: 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.
3.
Moudoud, H.; Houda, Z. Abou El; Brik, B.
Advancing Privacy and Fairness in Healthcare Using Federated Edge Learning and Blockchain Journal Article
In: IEEE Internet of Things Journal, vol. 12, no. 22, pp. 46129–46137, 2025, ISSN: 23274662 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence algorithms, Artificial intelligence techniques, Block-chain, Blockchain, cancer diagnosis, Diagnosis, Distributed computer systems, Federated edge learning, Health care, Healthcare, Healthcare systems, Internet of medical thing, Internet of Medical Things (IoMT), Learning systems, Medical computing, Medical data, Network security, Privacy, Sensitive data
@article{moudoudAdvancingPrivacyFairness2025,
title = {Advancing Privacy and Fairness in Healthcare Using Federated Edge Learning and Blockchain},
author = {H. Moudoud and Z. Abou El Houda and B. Brik},
url = {https://www.scopus.com/pages/publications/105012264671?origin=resultslist},
doi = {10.1109/JIOT.2025.3589179},
issn = {23274662 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Internet of Things Journal},
volume = {12},
number = {22},
pages = {46129–46137},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {artificial intelligence (AI) has revolutionized many fields, including healthcare. The adoption of AI techniques in critical healthcare tasks, such as cancer diagnosis, holds great promise for revolutionizing the healthcare system. AI algorithms can be trained on vast datasets to recognize patterns, detect anomalies, and provide accurate assessments. However, the lack of realistic and up-to-date medical data poses a significant challenge to the widespread adoption of AI techniques. Additionally, privacy concerns surrounding sensitive medical data, particularly patient health records (PHRs), hinder data sharing among healthcare practitioners. This article aims to address these challenges by proposing a novel framework, entitled SecureMed, that uses federated learning (FL) and Blockchain to preserve privacy in the healthcare system. In particular, SecureMed consists of: 1) a novel distributed architecture that enables secure collaboration among multiple mobile edge computing (MEC)-based Internet of Medical Things (IoMT) devices, while ensuring the privacy of healthcare systems; 2) a fairness-aware FL solution to ensure that model performance is balanced across all participating healthcare institutions, addressing the issue of imbalanced data contributions; 3) a secure multiparty computation (SMPC) protocol to ensure secure aggregation of local model updates; and 4) a blockchain-based reputation model for collaborative FL training. The proposed framework leverages smart contracts to ensure trustworthiness, decentralization, and transparency in the FL process. The experimental results using the CIC IoMT dataset 2024 highlight the promising potential of SecureMed in revolutionizing healthcare systems. © 2014 IEEE.},
keywords = {Artificial intelligence algorithms, Artificial intelligence techniques, Block-chain, Blockchain, cancer diagnosis, Diagnosis, Distributed computer systems, Federated edge learning, Health care, Healthcare, Healthcare systems, Internet of medical thing, Internet of Medical Things (IoMT), Learning systems, Medical computing, Medical data, Network security, Privacy, Sensitive data},
pubstate = {published},
tppubtype = {article}
}
artificial intelligence (AI) has revolutionized many fields, including healthcare. The adoption of AI techniques in critical healthcare tasks, such as cancer diagnosis, holds great promise for revolutionizing the healthcare system. AI algorithms can be trained on vast datasets to recognize patterns, detect anomalies, and provide accurate assessments. However, the lack of realistic and up-to-date medical data poses a significant challenge to the widespread adoption of AI techniques. Additionally, privacy concerns surrounding sensitive medical data, particularly patient health records (PHRs), hinder data sharing among healthcare practitioners. This article aims to address these challenges by proposing a novel framework, entitled SecureMed, that uses federated learning (FL) and Blockchain to preserve privacy in the healthcare system. In particular, SecureMed consists of: 1) a novel distributed architecture that enables secure collaboration among multiple mobile edge computing (MEC)-based Internet of Medical Things (IoMT) devices, while ensuring the privacy of healthcare systems; 2) a fairness-aware FL solution to ensure that model performance is balanced across all participating healthcare institutions, addressing the issue of imbalanced data contributions; 3) a secure multiparty computation (SMPC) protocol to ensure secure aggregation of local model updates; and 4) a blockchain-based reputation model for collaborative FL training. The proposed framework leverages smart contracts to ensure trustworthiness, decentralization, and transparency in the FL process. The experimental results using the CIC IoMT dataset 2024 highlight the promising potential of SecureMed in revolutionizing healthcare systems. © 2014 IEEE.
4.
Zhu, Y.; Falk, T.
WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model Journal Article
In: IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 9, pp. 6353–6365, 2025, ISSN: 21682194 (ISSN).
Abstract | Links | BibTeX | Tags: Agnostic, area under the curve, article, artificial neural network, asthma, autoencoder, Benchmarking, breathing, chronic obstructive lung disease, Computer-Assisted, controlled study, convolutional neural network, coronavirus disease 2019, Cross-domain, Databases, Diagnosis, Diagnostic, Diagnostic model, diagnostic test accuracy study, diagnostics, Differential privacy, Dynamics, dysarthria, Electronic health record, embedding, Embeddings, Factual, factual database, Generalizability, Health embedding, Health embeddings, Health monitoring, human, Humans, Machine learning, malignant neoplasm, model, Pathological speech, pathophysiology, physiology, pneumonia, Privacy, Privacy preserving, privacy preserving speech health diagnostic model, privacy-preserving, Privacy-preserving techniques, receiver operating characteristic, short time Fourier transform, Signal processing, speech, speech articulation, speech disorder, Speech Disorders, State of the art, temporal representation encoder, training, waveform
@article{zhuWavRxDiseaseAgnosticGeneralizable2025,
title = {WavRx: A Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model},
author = {Y. Zhu and T. Falk},
url = {https://www.scopus.com/pages/publications/85203439930?origin=resultslist},
doi = {10.1109/JBHI.2024.3454550},
issn = {21682194 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {29},
number = {9},
pages = {6353–6365},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizability across datasets. Furthermore, concerns have been raised recently towards the leakage of speaker identity from health embeddings. To mitigate these limitations, we propose WavRx, a speech health diagnostics model that captures the respiration and articulation related dynamics from a universal speech representation. Our in-domain and cross-domain experiments on six pathological speech datasets demonstrate WavRx as a new state-of-the-art health diagnostic model. Furthermore, we show that the amount of speaker identity entailed in the WavRx health embeddings is significantly reduced without extra guidance during training. An in-depth analysis of the model was performed, thus providing physiological interpretation of its improved generalizability and privacy-preserving ability. © 2013 IEEE.},
keywords = {Agnostic, area under the curve, article, artificial neural network, asthma, autoencoder, Benchmarking, breathing, chronic obstructive lung disease, Computer-Assisted, controlled study, convolutional neural network, coronavirus disease 2019, Cross-domain, Databases, Diagnosis, Diagnostic, Diagnostic model, diagnostic test accuracy study, diagnostics, Differential privacy, Dynamics, dysarthria, Electronic health record, embedding, Embeddings, Factual, factual database, Generalizability, Health embedding, Health embeddings, Health monitoring, human, Humans, Machine learning, malignant neoplasm, model, Pathological speech, pathophysiology, physiology, pneumonia, Privacy, Privacy preserving, privacy preserving speech health diagnostic model, privacy-preserving, Privacy-preserving techniques, receiver operating characteristic, short time Fourier transform, Signal processing, speech, speech articulation, speech disorder, Speech Disorders, State of the art, temporal representation encoder, training, waveform},
pubstate = {published},
tppubtype = {article}
}
Speech is known to carry health-related attributes, which has emerged as a novel venue for remote and long-term health monitoring. However, existing models are usually tailored for a specific type of disease, and have been shown to lack generalizability across datasets. Furthermore, concerns have been raised recently towards the leakage of speaker identity from health embeddings. To mitigate these limitations, we propose WavRx, a speech health diagnostics model that captures the respiration and articulation related dynamics from a universal speech representation. Our in-domain and cross-domain experiments on six pathological speech datasets demonstrate WavRx as a new state-of-the-art health diagnostic model. Furthermore, we show that the amount of speaker identity entailed in the WavRx health embeddings is significantly reduced without extra guidance during training. An in-depth analysis of the model was performed, thus providing physiological interpretation of its improved generalizability and privacy-preserving ability. © 2013 IEEE.
5.
Moudoud, H.; Houda, Z. A. El; Brik, B.; Jan, M. A.; Alshawi, B.
Advancing Robustness and Privacy in Federated Learning for Secure Autonomous Vehicle Systems Journal Article
In: IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 6183–6192, 2025, ISSN: 00983063 (ISSN).
Abstract | Links | BibTeX | Tags: Augmented intelligence of things, Autonomous driving, Autonomous vehicle system, Autonomous Vehicles, Critical challenges, Differential privacy, Distributed machine learning, Driving environment, Federated learning, Privacy, Security, Security and privacy, Vehicle system
@article{moudoudAdvancingRobustnessPrivacy2025,
title = {Advancing Robustness and Privacy in Federated Learning for Secure Autonomous Vehicle Systems},
author = {H. Moudoud and Z. A. El Houda and B. Brik and M. A. Jan and B. Alshawi},
url = {https://www.scopus.com/pages/publications/105002620945?origin=resultslist},
doi = {10.1109/TCE.2025.3558999},
issn = {00983063 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Consumer Electronics},
volume = {71},
number = {2},
pages = {6183–6192},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The rapid development of Autonomous Vehicle Systems (AVS) is transforming transportation, enabling safer, more efficient mobility. However, ensuring the security and privacy of sensitive data generated by AVS remains a major challenge. Federated Learning (FL) has emerged as a promising solution for AVS by enabling distributed machine learning across connected vehicles without sharing raw data, thereby enhancing privacy. Despite these advantages, FL faces critical challenges in autonomous driving environments, including high communication overhead, latency, and vulnerability to adversarial attacks. To address these challenges, we propose SecureFL, a novel framework designed to enhance the robustness and privacy of FL in autonomous vehicle systems. First, we propose a Federated Gradient Sign Attack (FGSA) detection mechanism using an ensemble of classifiers to identify and mitigate adversarial attacks that attempt to corrupt the global learning model. Then, we integrate a Graph Neural Network (GNN)-based reputation system that evaluates the reliability of vehicles based on data quality, prioritizing contributions from trustworthy sources, and dynamically adjusting participation in the FL process. Finally, we introduce an uplink scheduling mechanism utilizing a rate-splitting multiple access (RSMA) technique to optimize data transmission and reduce latency, ensuring efficient communication across the AVS network. The framework’s effectiveness is validated through simulations in real-world AVS environments, demonstrating SecureFL’s capability to strengthen security, privacy, and communication efficiency in federated learning for autonomous vehicles. This work contributes to advancing the robustness and privacy of FL, enabling safer and more secure autonomous driving. © 1975-2011 IEEE.},
keywords = {Augmented intelligence of things, Autonomous driving, Autonomous vehicle system, Autonomous Vehicles, Critical challenges, Differential privacy, Distributed machine learning, Driving environment, Federated learning, Privacy, Security, Security and privacy, Vehicle system},
pubstate = {published},
tppubtype = {article}
}
The rapid development of Autonomous Vehicle Systems (AVS) is transforming transportation, enabling safer, more efficient mobility. However, ensuring the security and privacy of sensitive data generated by AVS remains a major challenge. Federated Learning (FL) has emerged as a promising solution for AVS by enabling distributed machine learning across connected vehicles without sharing raw data, thereby enhancing privacy. Despite these advantages, FL faces critical challenges in autonomous driving environments, including high communication overhead, latency, and vulnerability to adversarial attacks. To address these challenges, we propose SecureFL, a novel framework designed to enhance the robustness and privacy of FL in autonomous vehicle systems. First, we propose a Federated Gradient Sign Attack (FGSA) detection mechanism using an ensemble of classifiers to identify and mitigate adversarial attacks that attempt to corrupt the global learning model. Then, we integrate a Graph Neural Network (GNN)-based reputation system that evaluates the reliability of vehicles based on data quality, prioritizing contributions from trustworthy sources, and dynamically adjusting participation in the FL process. Finally, we introduce an uplink scheduling mechanism utilizing a rate-splitting multiple access (RSMA) technique to optimize data transmission and reduce latency, ensuring efficient communication across the AVS network. The framework’s effectiveness is validated through simulations in real-world AVS environments, demonstrating SecureFL’s capability to strengthen security, privacy, and communication efficiency in federated learning for autonomous vehicles. This work contributes to advancing the robustness and privacy of FL, enabling safer and more secure autonomous driving. © 1975-2011 IEEE.
6.
Margulis, A.; Boeck, H.; Bendavid, Y.; Durif, F.
Building theory from consumer reactions to RFID: discovering Connective Proximity Journal Article
In: Ethics and Information Technology, vol. 18, no. 2, pp. 81–101, 2016, ISSN: 13881957 (ISSN), (Publisher: Springer Netherlands).
Abstract | Links | BibTeX | Tags: 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.
7.
Margulis, A.; Boeck, H.; Bendavid, Y.; Durif, F.
Building theory from consumer reactions to RFID: discovering Connective Proximity Journal Article
In: Ethics and Information Technology, vol. 18, no. 2, pp. 81–101, 2016, ISSN: 13881957 (ISSN).
Abstract | Links | BibTeX | Tags: 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.