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Normative challenges of identification in the Internet of Things: Privacy, profiling, discrimination, and the GDPR
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“Similarly, Article 20's right to data portability, permitting the transfer of personal data, can affect algorithmic accuracy and confidentiality, potentially influencing the larger dataset.”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [161]
In the citing paper
“Article 21's right to object enables individuals to halt data processing, ensuring data minimization and protecting against data misuse.”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [161]
In the citing paper
“Article 22, crucial in the context of AI, prohibits automated decisions if they produce significant effects.”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [161]
In the citing paper
“Article 22-4 prohibits using sensitive data for automated decisions, with an exception.”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [161]
In the citing paper
“Similarly, non-sensitive data can act as proxies for sensitive data, leading to possible unlawful discrimination [161].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [161]
In the citing paper
“Article 22-2 provides exceptions, allowing automated decisions if they are contractually necessary, legally authorized with safeguards, or based on explicit consent.”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [161]
In the citing paper
“For instance, sexual orientation can be inferred from seemingly unrelated data like activity or likes.”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [161]
In the citing paper
“Yet, AI can infer sensitive data from non-sensitive data, potentially leading to unauthorized processing.”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [161]
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Items 101–200 of 234
Internet of Things (IOT)-Enabled Product Monitoring at Steadyserv
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Celdrán, A. H., Pérez, M. G., Mlakar, I., et al. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-1301-5_6 · Cited by 6
An Edge Decentralized Security Architecture for Industrial IoT Applications
Portal, G., de Matos, É., & Hessel, F. P. (2020) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot48130.2020.9221176 · Cited by 7
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Data Transparency Design in Internet of Things: A Systematic Review
Long, Y., Luo, X., Zhu, Y., Lee, K.-P., & Wang, S. J. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2228997 · Cited by 5
“As a consequence, privacy should be transformed from service-oriented (Ziegeldorf et al., 2014) to user-centric (Wachter, 2018).”
PROTECTOR: Towards the protection of sensitive data in Europe and the US
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“Data subject identification is an ongoing discussion (see, for example, multi-user IoT devices [17] and web tracking through shadow profiles [5]), and there is much potential for future work in the space.”
BackgroundEnhancing data governance through transparency: An empirical study of the data trust model
Kim, Y. J., Park, Y. S., & Park, S.-P. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124551 · Cited by 5
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A Systematic Mapping Study of Access Control in the Internet of Things
Ragothaman, K. N. M., & Wang, Y. (2021) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2021.853 · Cited by 4
“Given the heterogeneity and the need to protect the information that IoT devices collect, device identities need to be addressed before access control [39].”
BackgroundSecurity Risk Assessment of Blockchain-Based Patient Health Record Systems
Barghuthi, N. B. A., Said, H., Badi, S. M., & Girija, S. (2023) · Lecture notes in business information processing · doi:10.1007/978-3-031-30694-5_35 · Cited by 4
Safeguarding the Smart Home: Heterogeneous Federated Deep Learning for Intrusion Defense
Shalan, M., Li, J., & Bai, Y. Z. (2024) · IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) · doi:10.1109/mobilecloud62079.2024.00010 · Cited by 5
Addressing Data Scarcity with Synthetic Data: A Secure and GDPR-Compliant Cloud-Based Platform
Borovits, N., Bardelloni, G., Hashemi, H., Tulsiani, M., Tamburri, D. A., & van den Heuvel, W. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3732937 · Cited by 4
“Central to the success of these technologies is the quality and volume of data, which significantly impact the performance of ML and DL models.”
Digital Social Contracts with AI Robots: Some Implications for Amazon.Com
Lorne, F. T., & Gogireddy, M. R. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-77246-8_8 · Cited by 4
van Grunsven, J. (2022) · Springer eBooks · doi:10.1007/978-3-030-88793-3_4 · Cited by 4
Privacy and Security Evaluation of Amazon Echo Voice Assistant
Alotaibi, N., & Lombardi, F. (2021) · International Conference of Women in Data Science at Taif University (WiDSTaif) · doi:10.1109/widstaif52235.2021.9430217 · Cited by 4
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Exploring IoT privacy compliance research: a topic modeling approach
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Vehicle Availability Profiling from Diverse Data Sources
Naylor, S., Pinchin, J., Gough, R., et al. (2019) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2019.8730716 · Cited by 3
“Increasing awareness of the issues around data privacy and its implications in data-analysis applications are reflected in recent policy changes [6].”
BackgroundAn Intelligent and Scalable IoT Monitoring Framework for Safety in Civil Construction Workspaces
Ferreira, C., Correia, L., Lopes, M., et al. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-14859-0_6 · Cited by 3
Anonymization Use Cases for Data Transfer in the Automotive Domain
Fieschi, A., Hirmer, P., Sturm, R., Eisele, M., & Mitschang, B. (2023) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops56833.2023.10150357 · Cited by 3
“Great data mining and learning opportunities are often missed because of the difficulties of collecting data while preserving the user’s privacy or to meet the legal requirements established for the same purpose, such as the European General Data Protection Regulation (GDPR) [2], [3] .”
BackgroundETHICAL COMPLIANCE OF AI TOOLS IN INDUSTRIAL MANUFACTURING
Guillén, A., Fischer, C., Teodoro, E., & Gurzawska, A. (2023) · ECCOMAS Thematic Conference on Smart Structures and Materials (SMART) · doi:10.7712/150123.9877.450825 · Cited by 3
Goode, S., & Riaz, A. (2023) · Information Technology and People · doi:10.1108/itp-06-2022-0416 · Cited by 3
Data Protection Around the World: Future Challenges
Cortez, E. K. (2020) · Information technology and law series/Information technology & law series · doi:10.1007/978-94-6265-407-5_12 · Cited by 3
Strangers in the Room: Unpacking Perceptions of 'Smartness' and Related Ethical Concerns in the HomePreprint
Seymour, W., Binns, R., Kleek, M. G. V., Slovák, P., & Shadbolt, N. (2019) · arXiv (Cornell University) · doi:10.17605/osf.io/c3aes · Cited by 3
The tech company dilemma : navigating government requests for user data
Ebert, I. L. (2021) · Alexandria (UniSG) (University of St.Gallen) · Cited by 3
Posso, R., & Altmann, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-81226-2_19 · Cited by 2
“Fig.4(a) shows that in TPP the most influential trust dimensions are security [17, 39–50] [51–55], integrity [17, 40, 46, 48, 50, 52, 56– 63],authorization [15, 17, 20, 48, 50, 51, 61, 64–66], and authentication [15, 20, 22, 45, 48, 50, 56, 61, 66, 67].”
A Model for Balancing Data Security and Privacy in IoT Applications
Sen, A. A. A., Mnaouer, A. B., Tayan, O., et al. (2025) · Global Information Infrastructure and Networking Symposium · doi:10.1109/giis64151.2025.10921948 · Cited by 2
“These two problems are still in doubt, especially after many scandals involving large companies that illegal exploit the data of their users [8].”
Some Aspects of Using the Concept of «the Internet of Things» in Crime Counteraction
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Standards and Technologies from Building Sector, IoT, and Open-Source Trends
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Inclusive Privacy Control at Home for Smart Health
Manohar, A. K., Sengul, C., & Chen, J. (2023) · Postdigital science and education · doi:10.1007/978-3-031-31875-7_9 · Cited by 2
Information Privacy: Not Just GDPR
Bruschi, D. (2019) · ODU Digital Commons (Old Dominion University) · doi:10.25884/6n7z-h173 · Cited by 2
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Applications and Security Aspects of Internet of Things for Way of Smart World
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Personality and Individual Differences
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Data Protection in the Age of Big DataPreprint
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Avrupa Birliği Genel Veri Koruma Tüzüğü Kapsamında Getirilen Yeni Teknik ve Yaptırım Mekanizmaları
BULUT, İ. Ç. (2020) · Anadolu Üniversitesi Sosyal Bilimler Dergisi · doi:10.18037/ausbd.758041 · Cited by 1
Data Protection—Putting the Brakes on Digitalization Processes?
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A Study on Internet of Things: Applications, Security and Protection
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Nudging Data Privacy of Mobile Health Applications in Saudi Arabia
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An LGPD Compliance Inspection Checklist to Assess IoT Solutions
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User-Centric Tensions: Exploring Perceived Benefits and (Dis)comfort in Media Personalisation
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Disordered Eating, Food Landscapes, and Public Health
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Harnessing the Internet of Behavior (IoB) for Food Safety and Quality
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An Overview of Recent Development in Privacy Regulations and Future Research Opportunities
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“Data processing principles and data protection standards are strictly defined in GDPR [18].”
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Architectural Software Patterns for the Development of IoT Smart ApplicationsPreprint
Borelli, F., Biondi, G., Horita, F., & Kamienski, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04781 · Cited by 9
“The European General Data Protection Regulation (GDPR) adds urgency to the need for providing strict privacy guarantees to users of IoT applications [110].”
BackgroundSocial Interaction and Interpersonal Relationships
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Efficient Flood Risk Mitigation and Intersectional Equity Implications: A Case Study in New OrleansPreprint
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Datenschutzkonzepte für Zeitreihendaten
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Wireless Sensor Networks in IPM
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“Although much research has focused on demonstrating usability of these tool [2] [3][4] on various technologies and interfaces [5][6] [7] [8], they have often been shown to be ineffective [9] [10] [11], due to their complexity. and incomprehensibility [12] [13] [14][15].”
BackgroundHardware and Software Solution for Preserving Privacy of Mobile Devices and their Applications
Basahel, A. M., Sen, A. A. A., Yamin, M., & Bahbouh, N. M. (2021) · International Conference on Computing for Sustainable Global Development · doi:10.1109/indiacom51348.2021.00069
“In 2018, General Data Protection Regulation (GDPR) [22] was enforced in Europe countries.”
BackgroundLearning Analytics Through Internet of Things for Better Learning
Bakla, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-85720-2_8
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Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.009
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An Overview of Recent Development in Privacy Regulations and Future Research Opportunities
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Algorithmic Social Sorting and New Legal Narratives on Digital Privacy
Stepanović, I. (2022) · Zbornik instituta za kriminološka i sociološka istraživanja · doi:10.47152/ziksi2022036
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Kupec, V., Švec, M., & Madleňák, A. (2022) · AD ALTA Journal of Interdisciplinary Research · doi:10.33543/1202152157
“The basis for any legitimate processing of personal data of natural persons concerned is the existence of relevant legal basis under the GDPR (Wachter, 2018).”
BackgroundThe Pivotal Role of DPAs in Digital Privacy Protection
Popović, D. V. (2022) · Law Identity and Values · doi:10.55073/2022.2.131-146
PAPA for IoT - Role of Data Accuracy in IoT Deployment and Data-centric Decision-Making
Vermanen, M., Naskali, J., Harkke, V., & Koskinen, J. S. S. (2023) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro57284.2023.10159749
“The issue of combining data that is professionally irrelevant also needs to be brought up, including situations where the border between professional and personal is being crossed [17], as this enables companies to conduct ethically questionable profiling [18].”
BackgroundUser-Centred Detection of Violent Conversations on Mobile Edge Devices
Anwar, A., & Kanjo, E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34609-5_25
Negotiating privacy and trust in the geoaware smart city
Rattle, R. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.242467
Patel, S., & Patel, R. M. (2023) · American Journal of Bioscience and Bioengineering · doi:10.11648/j.bio.20231105.14
Privacy Respecting Design Principles for Smart Home DevicesPreprint
Murphy, R., Keedwell, E. C., & Hartley, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4643647
Responses of the European IoT Ecosystem to the European General Data Protection RegulationPreprint
Ebbers, F., & Friedewald, M. (2023) · Research Square · doi:10.21203/rs.3.rs-3706521/v1
Chiara, P. G. (2024) · Law, governance and technology series · doi:10.1007/978-3-031-67663-5_7
Range Proof-Based Noise Filtering Mechanism for loT Differential Privacy
Wei, J., Chen, Y., Luo, Y., & Li, Z.-D. (2024) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/cyberscitech64112.2024.00025
“High computational needs for public data sets’ cryptography present challenges for efficient, secure IoT data management [6].”
Coding the Efficient Privacy Policy: Striking a Balance between Lexical Density and Readability
Alić, M. (2024) · WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS · doi:10.37394/23207.2024.21.213
From Cyber Frameworks to Autonomous Defense: A U.S.-Centric Model for AI-Integrated Compliance
Marapu, N. R. (2023) · International Journal of Emerging Research in Engineering and Technology · doi:10.63282/3050-922x.ijeret-v4i1p109
Metadata
- Authors
- Wachter, S.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Wachter, Sandra
- ORCID
- Wachter, Sandra
- Venue
- Computer Law & Security Review
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 34
- Issue
- 3
- Pages
- 436-449
- ISSN
- 2212-473X (ISSN-L)
- OpenAlex
- W3125356411
- Cited by
- 307
- References
- 29
- Citations by year
- 2026: 6 · 2025: 42 · 2024: 25 · 2023: 36 · 2022: 36 · 2021: 74 · 2020: 54 · 2019: 28 · 2018: 6
- FWCI
- 247.80
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Computer law & security review (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- Internet of Things, user identification, access control, privacy, profiling, discrimination, General Data Protection Regulation, data protection law, identity control, consent, inference, data anonymization, cybersecurity, transparency, trust, IoT device design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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