The DOI matches its Crossref record; 18 of the 29 references it cites were found in a registry or an index.
Normative challenges of identification in the Internet of Things: Privacy, profiling, discrimination, and the GDPR
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Context Aware Computing for The Internet of Things: A Survey
Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.042313.00197 · Cited by 2,861
A survey on trust management for Internet of Things
Yan, Z., Zhang, P., & Vasilakos, A. V. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2014.01.014 · Cited by 1,155
Dynamic consent: a patient interface for twenty-first century research networks
Kaye, J., Whitley, E. A., Lund, D., Morrison, M., Teare, H., & Melham, K. (2015) · European Journal of Human Genetics · doi:10.1038/ejhg.2014.71 · Cited by 780
Huckvale, K., Prieto, J. T., Tilney, M., Benghozi, P.-J., & Car, J. (2015) · BMC Medicine · doi:10.1186/s12916-015-0444-y · Cited by 376
Student privacy in learning analytics: An information ethics perspective
Rubel, A., & Jones, K. M. L. (2016) · The Information Society · doi:10.1080/01972243.2016.1130502 · Cited by 291
Ethical Issues of Social Media Usage in Healthcare
Bamidis, P., Bond, C., Gabarron, E., Househ, M., Lau, A. Y. S., Mayer, M. A., Merolli, M., Hansen, M., & Denecke, K. (2015) · Yearbook of Medical Informatics · doi:10.15265/iy-2015-001 · Cited by 199
Internet of things: Privacy issues revisited
Weber, R. (2015) · Computer Law and Security Review · doi:10.1016/j.clsr.2015.07.002 · Cited by 187
From individual to group privacy in big data analytics
Mittelstadt, B. (2017) · Philosophy & Technology · doi:10.1007/s13347-017-0253-7 · Cited by 184
Mantelero, A. (2016) · Computer law & security review · doi:10.1016/j.clsr.2016.01.014 · Cited by 167
Ethics of the health-related internet of things: a narrative review
Mittelstadt, B. (2017) · Ethics and Information Technology · doi:10.1007/s10676-017-9426-4 · Cited by 154
Cited by
Sorted by citations
Ismagilova, E., Hughes, L., Rana, N. P., & Dwivedi, Y. K. (2020) · Information Systems Frontiers · doi:10.1007/s10796-020-10044-1 · Cited by 558
“Wachter (2018) argues that GDPR standards need further specification and implementation into the design of IoT technologies.”
BackgroundA Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI
Wachter, S., & Mittelstadt, B. (2018) · doi:10.31228/osf.io/mu2kf · Cited by 463
Self-citationAhmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2021.100452 · Cited by 291
A critical review of algorithms in HRM: Definition, theory, and practice
Cheng, M. M., & Hackett, R. D. (2021) · Human Resource Management Review · doi:10.1016/j.hrmr.2019.100698 · Cited by 283
Discrimination, artificial intelligence, and algorithmic decision-making
Borgesius, F. Z. (2025) · arXiv · doi:10.48550/arxiv.2510.13465 · Cited by 152
“See for instance: Goodman 2016; Mantelero 2018; Hacker 2018; Hoboken and Kostic (forthcoming); Wachter 2018; Wachter and Mittelstadt 2018.”
Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152
“Similarly, non-sensitive data can act as proxies for sensitive data, leading to possible unlawful discrimination [161].”
BackgroundNudging citizens through technology in smart cities
Ranchordás, S. (2019) · International Review of Law Computers & Technology · doi:10.1080/13600869.2019.1590928 · Cited by 140
“Nevertheless, despite claims that cameras and microphones are mostly used for crowd management and that information will be anonymized, the risk of pro fi ling lurks around the corner (Wachter 2018).”
BackgroundMethodInfluentialEthical issues in learning analytics: a review of the field
Tzimas, D., & Demetriadis, S. (2021) · Educational Technology Research and Development · doi:10.1007/s11423-021-09977-4 · Cited by 119
“The GDPR standards relate to informed consent, privacy-by-design, algorithmic transparency, and automated decision-making (Wachter, 2018), so the GDPR provides privacy and control protection for LA users.”
BackgroundYuan, B., & Li, J. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16061070 · Cited by 125
“Patients cannot control their data, and do not have sufficient levels of awareness to give free and informed consent [3].”
BackgroundKirk, H. R., Vidgen, B., Röttger, P., & Hale, S. A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00820-y · Cited by 122
“This is similar to any technology that relies on personal information to deliver tailored benefits 99 , such as with the Internet of Things 100 or targeted advertising 101,102 .”
Social equity in the data era: A systematic literature review of data‐driven public service research
Ruijer, E. H. J. M., Porumbescu, G. A., Porter, R., & Piotrowski, S. J. (2022) · Public Administration Review · doi:10.1111/puar.13585 · Cited by 132
Access Control for IoT: A Survey of Existing Research, Dynamic Policies and Future Directions
Ragothaman, K. N. M., Wang, Y., Rimal, B. P., & Lawrence, M. (2023) · Sensors · doi:10.3390/s23041805 · Cited by 107
“Given the heterogeneity and the need to protect the data that IoT devices collect, IDoT needs to be addressed before access control [105].”
BackgroundDeterminants of customers' intentions to use hedonic networks: The case of Instagram
Bahri-Ammari, N., Hadhri, W., & Mezrani, M. (2018) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2018.09.001 · Cited by 101
Pal, D., Zhang, X., & Siyal, S. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101683 · Cited by 84
Internet of behaviours (IoB) and its role in customer services
Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R. (2021) · Sensors International · doi:10.1016/j.sintl.2021.100122 · Cited by 80
Data Protection in the Age of Big Data
Wachter, S. (2019) · Nature Electronics · doi:10.1038/s41928-018-0193-y · Cited by 78
Self-citationA mapping of IoT user-centric privacy preserving approaches to the GDPR
Kounoudes, A. D., & Kapitsaki, G. M. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100179 · Cited by 82
GDPR Compliance Verification in Internet of Things
Barati, M., Rana, O., Petri, I., & Theodorakopoulos, G. (2020) · IEEE Access · doi:10.1109/access.2020.3005509 · Cited by 72
“INTRODUCTION The rapid growth in the usage of Internet of Things (IoT) devices has led to the emergence of various IoT-based applications in domains such as energy consumption and utility monitoring, smart buildings, transportation, healthcare and assisted living environments [1].”
BackgroundDifferential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey
Husnoo, M. A., Anwar, A., Chakrabortty, R. K., Doss, R., & Ryan, M. J. (2021) · IEEE Access · doi:10.1109/access.2021.3124309 · Cited by 74
“Furthermore, several researchers have also proposed data anonymization techniques for data privacy preservation in IoT-enabled CIs [16].”
BackgroundCyberbullying among Adolescents: Psychometric Properties of the CYB-AGS Cyber-Aggressor Scale
Buelga, S., Postigo, J., Martínez–Ferrer, B., Cava, M. J., & Ortega-Barón, J. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17093090 · Cited by 74
“The connection speed will be up to 100 times faster than the current one [1], and this change will make it possible to optimize an extensive environment of everyday objects connected to the Internet; Internet of Things (IoT) [2,3].”
BackgroundA New Method With Swapping of Peers and Fogs to Protect User Privacy in IoT Applications
Yamin, M., & Sen, A. A. A. (2020) · IEEE Access · doi:10.1109/access.2020.3038825 · Cited by 59
“More details, including the principles of the GDPR, are available in [42]–[44].”
BackgroundLibaque-Sáenz, C. F., Wong, S. F., Chang, Y., & Bravo, E. (2020) · Information & Management · doi:10.1016/j.im.2020.103284 · Cited by 61
Reconciliation of Privacy with Preventive Cybersecurity: The Bright Internet Approach
Lee, J. K., Chang, Y., Kwon, H.-Y., & Kim, B. (2020) · Information Systems Frontiers · doi:10.1007/s10796-020-09984-5 · Cited by 45
“Among such privacy policies, the European Union’s GDPR has the most comprehensive jurisdiction (Politou et al. 2018; Wachter 2018).”
BackgroundRegulating AI and Robotics: Ethical and Legal Challenges
Ebers, M. (2020) · Algorithms and Law · doi:10.1017/9781108347846.003 · Cited by 44
The General Data Protection Regulation: Requirements, Architectures, and Constraints
Hjerppe, K., Ruohonen, J., & Leppänen, V. (2019) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2019.00036 · Cited by 56
“While together A6(1a) and A7 imply a clear requirement for the case company, it should be underlined that the company’s compliance is not relying on A6(1f) and its much debated [31] concept of so-called legitimate interests.”
BackgroundUser-centric privacy preserving models for a new era of the Internet of Things
Rivadeneira, J. E., Silva, J. S., Colomo‐Palacios, R., Rodrigues, A., & Boavida, F. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103695 · Cited by 41
Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey
Qi, M., Wang, Z., Han, Q.-L., Zhang, J., Chen, S., & Xiang, Y. (2024) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.106058 · Cited by 44
To trust or not to trust smart consumer products: a literature review of trust-building factors
Michler, O., Decker, R., & Stummer, C. (2019) · Management Review Quarterly · doi:10.1007/s11301-019-00171-8 · Cited by 49
“The request for more transparency was also among the drivers for the new EU General Data Protection Regulation (GDPR), which came into force in 2018 (Wachter 2018).”
BackgroundAssche, E. V., Ramos‐Quiroga, J. A., Pariante, C. M., Sforzini, L., Young, A. H., Flossbach, Y., Gold, S. M., Hoogendijk, W. J. G., Baune, B. T., & Maron, E. (2022) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2022.05.007 · Cited by 40
Vehicle Telematics for Safer, Cleaner and More Sustainable Urban Transport: A Review
Ghaffarpasand, O., Burke, M. J., Osei, L. K., Ursell, H., Chapman, S., & Pope, F. D. (2022) · Sustainability · doi:10.3390/su142416386 · Cited by 50
The myth of individual control: Mapping the limitations of privacy self-managementPreprint
Kröger, J. L., Lutz, O. H.-M., & Ullrich, S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3881776 · Cited by 43
Zaguir, N. A., de Magalhães, G. H., & Spínola, M. D. M. (2024) · IEEE Access · doi:10.1109/access.2024.3406724 · Cited by 37
“Wachter [96] highlight that identification and access con-trol technologies are essential infrastructure for linking data between a user’s devices with unique identities and providing integrated, linked services.”
BackgroundRenya Nath, N., & Nath, H. V. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107997 · Cited by 35
Quinn, P. F., & Malgieri, G. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3713134 · Cited by 35
General Data Protection Regulation (GDPR) in Healthcare: Hot Topics and Research Fronts
Fatehi, F., Hassandoust, F., Ko, R. K. L., & Akhlaghpour, S. (2020) · Studies in health technology and informatics · doi:10.3233/shti200336 · Cited by 35
“In spite of the fact that GDPR is a recently developed legal instrument, there have been many research studies on GDPR since its adoption in 2016, such as [4; 5].”
BackgroundAuthenticated and Privacy-Preserving Consent Management in the Internet of Things
Laurent, M., Leneutre, J., Chabridon, S., et al. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.04.037 · Cited by 32
“Following the analysis conducted in [15], this use case is representative of personalization services exploiting a user’s profile to provide a relevant service tailored to the needs of this user.”
MethodOnline Customer Trust in the Context of the General Data Protection Regulation (GDPR)
Zhang, J., Hassandoust, F., & Williams, J. E. (2020) · Pacific Asia journal of the Association for Information Systems · doi:10.17705/1pais.12104 · Cited by 43
“It is expected to provide opportunities for organizations to improve online customer trust through risk-related personal data governance (European Commission, 2019c; Wachter, 2018).”
BackgroundLightweight Data-Security Ontology for IoT
Gonzalez-Gil, P., Martinez, J. A., & Skármeta, A. (2020) · Sensors · doi:10.3390/s20030801 · Cited by 31
“Finally, public and political concern has coalesced in the form of public regulations, such as the General Data Protection Regulation (GDPR), which impose a new set of difficulties and restrictions on the way in which personal data is handled, that directly affects the IoT [12].”
BackgroundCollaborative Working Architecture for IoT-Based Applications
Mora, H., Pont, M. T. S., Gil, D., & Johnsson, M. (2018) · Sensors · doi:10.3390/s18061676 · Cited by 31
“User privacy challenges arise even when generating information or inferences about the user behaviour [47].”
BackgroundMethodLegal challenges of digitalization and automation in the context of Industry 4.0
Habrat, D. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.10.132 · Cited by 30
User Privacy Concerns in Commercial Smart Buildings1
Harper, S., Mehrnezhad, M., & Mace, J. C. (2022) · Journal of Computer Security · doi:10.3233/jcs-210035 · Cited by 29
Shalan, M., Hasan, M. R., Bai, Y. Z., & Li, J. (2025) · Smart Cities · doi:10.3390/smartcities8010035 · Cited by 29
Privacy of IoT-Enabled Smart Home Systems
Dasgupta, A., Gill, A. Q., & Hussain, F. K. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.84338 · Cited by 29
Electronic identity services as sociotechnical and political-economic constructs
van Dijck, J., & Jacobs, B. (2019) · New Media & Society · doi:10.1177/1461444819872537 · Cited by 28
“…in 2018, the EU countries set a strong international norm as to which minimum data protection standards digital platforms need to comply with if they want to operate in the European digital realm; this standard has led to deliberation about sometimes conflicting norms and values (Wachter, 2018).”
BackgroundScherer, A. G., & Neesham, C. (2023) · Organization Theory · doi:10.1177/26317877231204083 · Cited by 30
“Scholars are warning against privacy threats, and also against the potential for extending social control and political manipulation to non-sub-scribers (Lanier, 2018; Wachter, 2018; Zuboff, 2019).”
BackgroundEthics in educational technology research: Informing participants on data sharing risks
Beardsley, M., Santos, P., Hernández‐Leo, D., & Michos, K. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12781 · Cited by 39
Davola, A., Querci, I., & Romani, S. (2022) · Journal of Consumer Policy · doi:10.1007/s10603-022-09530-7 · Cited by 29
“…and processing methods implemented in profiling algorithms has been thoroughly inspected (Dobber et al., 2019; Eskens, 2016; Malgieri, 2021; Wachter, 2018); also, competition law was identified as a potential tool to tackle the structural distortions caused by personalized practices…”
MethodA Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems
Dasgupta, A., Gill, A. Q., & Hussain, F. K. (2019) · International Conference on Data Science, Technology and Applications (DATA) · doi:10.5220/0007924302090216 · Cited by 27
“This can be attributed to the changes in regulations (Wachter, 2018) as well as advancement in technologies such as Big Data, Blockchain, Cloud, IoT and Mobile (Copie et al., 2013).”
BackgroundGDPR-Based User Stories in the Access Control Perspective
Bartolini, C., Daoudagh, S., Lenzini, G., et al. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-29238-6_1 · Cited by 25
Schneider, B., Reilly, J., & Radu, I. W. (2020) · Journal for STEM Education Research · doi:10.1007/s41979-020-00027-x · Cited by 24
Seymour, W., Binns, R., Slovák, P., Kleek, M. G. V., & Shadbolt, N. (2020) · Designing Interactive Systems Conference · doi:10.1145/3357236.3395501 · Cited by 26
“smart homes in general threaten principles of accountability [3], or discrimination [66]).”
BackgroundDigital ethics: Global trends and divergent paths
Guenduez, A., Walker, N., & Demircioglu, M. (2025) · Government Information Quarterly · doi:10.1016/j.giq.2025.102050 · Cited by 25
Norval, C., Janssen, H., Cobbe, J., et al. (2018) · doi:10.1145/3267305.3274153 · Cited by 22
“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.”
BackgroundBest practices for studies using digital data donation
Carrière, T. C., Boeschoten, L., Struminskaya, B., et al. (2024) · Quality & Quantity · doi:10.1007/s11135-024-01983-x · Cited by 22
“…entity processing personal data, such as digital platforms) are obliged to provide a copy of all personal data (i.e., data relating to an identified or identifiable person) they collected on their users upon request by the person to whom the data per-tains (Veale and Ausloos 2021; Wachter 2018).”
Ethical Challenges in the Use of Digital Technologies: AI and Big Data
Wylde, V., Prakash, E., Hewage, C. T. E. R., & Platts, J. (2023) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-031-09691-4_3 · Cited by 24
The trust gap: Social perceptions of privacy data for energy services in the United Kingdom
Grünewald, P., & Reisch, T. (2020) · Energy Research & Social Science · doi:10.1016/j.erss.2020.101534 · Cited by 22
Ethical framework for IoT deployment in SMEs: individual perspective
Vermanen, M., Rantanen, M. M., & Harkke, V. (2021) · Internet Research · doi:10.1108/intr-08-2019-0361 · Cited by 20
“Some of the most important risk factors to consider are that IoT solutions often enable collecting and combining data that is professionally irrelevant, yet allow the observer to practice profiling (Wachter, 2018) and draw personal conclusions about the observed individuals.”
BackgroundVojković, G., Milenković, M., & Katulić, T. (2020) · Business Systems Research Journal · doi:10.2478/bsrj-2020-0033 · Cited by 22
““Users’ right to data protection and right to privacy must be balanced in the design and governance of identification technologies in the IoT” (Wachter, 2018b).”
BackgroundDPIA in Context: Applying DPIA to Assess Privacy Risks of Cyber Physical Systems
Henriksen-Bulmer, J., Faily, S., & Jeary, S. (2020) · Future Internet · doi:10.3390/fi12050093 · Cited by 20
“For example, in regards to discrimination, one of the areas identified as one of the four challenges by Wachter [4], staff felt that potential discrimination was unlikely as this was an area staff and volunteers received a lot of guidance and training on.”
BackgroundInfluentialDoes the learning ability of smart products lead to user resistance?
Chouk, I., & Mani, Z. (2022) · Journal of Engineering and Technology Management · doi:10.1016/j.jengtecman.2022.101706 · Cited by 16
Maturity Model for IoT Adoption in Hospitals
Hasić, F., Beirens, B., & Serral, E. (2022) · Computing and informatics · doi:10.31577/cai_2022_1_213 · Cited by 15
Automatic Detection of Sensitive Data Using Transformer- Based Classifiers
Petrolini, M., Cagnoni, S., & Mordonini, M. (2022) · Future Internet · doi:10.3390/fi14080228 · Cited by 15
Karampela, M., Ouhbi, S., & Isomursu, M. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856550 · Cited by 15
“be considered obvious for everyday users [49].”
Enhancing User Privacy in IoT: Integration of GDPR and Blockchain
Barati, M., & RANA, O. F. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-2777-7_26 · Cited by 18
Smartphones within Psychological Science
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408 · Cited by 14
Participatory Data Design: Managing Data Sovereignty in IoT Solutions
Bowen, J., & Hinze, A. (2022) · Interacting with Computers · doi:10.1093/iwc/iwac031 · Cited by 14
“Ensuring users have control over how their data is used, and by whom is also gaining traction, particularly in light of legislative controls such as the EU General Data protection Regulation (GDPR) (European Union 2016).”
BackgroundPlatform governance in the era of AI and the digital economy
Zheng, X.-L., Zhou, G., & Zeng, D. (2023) · Frontiers of Engineering Management · doi:10.1007/s42524-022-0241-1 · Cited by 13
“For example, Germany’s NetzDG (Gorwa, 2021) and the EU’s General Data Protection Regulation (GDPR) (Wachter, 2018) require social media platforms with more than two million users to address inaccurate or inappropriate statements on their platforms within a certain time limit and report on their…”
BackgroundInfluentialProtected or Porous: A Comparative Analysis of Threat Detection Capability of IoT Safeguards
Mandalari, A. M., Haddadi, H., Dubois, D. J., et al. (2023) · Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy · doi:10.1109/sp46215.2023.10179282 · Cited by 14
“According to best practices and some privacy regulations [49], network traffic should be sent encrypted.”
BackgroundUnobtrusive Continuous Stress Detection in Knowledge Work—Statistical Analysis on User Acceptance
Kallio, J., Vildjiounaite, E., Kantorovitch, J., Kinnula, A., & López, M. B. (2021) · Sustainability · doi:10.3390/su13042003 · Cited by 14
“Since the invocation of sensors and cloud-based data analytics is growing, it is crucial to investigate the GDPR directive regarding the design and implementation of data-driven solutions in order to minimize conflicts between GDPR and the deployment of sensors and tracking softwarebased well-being monitoring [54].”
BackgroundAdaptive User Profiling in E-Commerce and Administration of Public Services
Gatziolis, K., Tselikas, N. D., & Moscholios, I. D. (2022) · Future Internet · doi:10.3390/fi14050144 · Cited by 14
Quantifying Bytes: Understanding Practical Value of Data Assets in Federated Learning
Yao, M., Qi, S., Tian, Z., Li, Q., Han, Y., Li, H., & Qi, Y. (2024) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010034 · Cited by 13
Mühlhoff, R. (2020) · Deutsche Zeitschrift für Philosophie · doi:10.1515/dzph-2020-0059 · Cited by 8
Oppl, S., & Stary, C. (2022) · Sustainability · doi:10.3390/su14020900 · Cited by 15
“Wachter [18] identified four user privacy challenges in GDPR: (1) profiling, inference, and discrimination; (2) control and context-sensitive sharing of identity; (3) consent and uncertainty; and (4) honesty, trust, and transparency.”
BackgroundNudging Citizens through Technology in Smart CitiesPreprint
Ranchordás, S. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3333111 · Cited by 13
Abu-Salma, R., Choy, J., Frik, A., & Bernd, J. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3702321 · Cited by 13
Colomb, Y., White, P., Islam, R., et al. (2022) · Springer eBooks · doi:10.1007/978-3-031-09640-2_7 · Cited by 12
Krpan, D., & Urbaník, M. (2021) · Behavioural Public Policy · doi:10.1017/bpp.2021.40 · Cited by 16
Contextualized BERT Sentence Embeddings for Author Profiling: The Cost of Performances
Polignano, M., de Gemmis, M., & Semeraro, G. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58811-3_10 · Cited by 16
“As a result of the increasingly stringent laws regarding the privacy of users GDPR [35], it is always less possible to access and use the data provided directly by users such as date of birth, gender, working position.”
BackgroundTowards data privacy in a fog of things
Pinto, G. P., & Prazeres, C. V. S. (2024) · Internet Technology Letters · doi:10.1002/itl2.512 · Cited by 11
“9 Then, we present an implementation of a simulated usage scenario using the Social Linked Data (Solid) specification for data storage.”
Swarnkar, M., & Kumar, R. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3373887 · Cited by 11
“Various methods for IoT device identification [9], [10], [11], [12] are proposed in the literature.”
MethodAutomated Authentication of Large-Scale IoT Devices with Hybrid Feature Selection
Chen, Q., Song, Y., Hu, A., et al. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-78621-2_55 · Cited by 11
Internet of Things and Artificial Intelligence in National Security: Applications and Issues
Montasari, R. (2023) · Advances in information security · doi:10.1007/978-3-031-21920-7_3 · Cited by 12
A readiness assessment tool for GDPR compliance certification
Chatzipoulidis, A., Tsiakis, T., & Kargidis, T. (2019) · Computer Fraud & Security · doi:10.1016/s1361-3723(19)30086-7 · Cited by 11
Toward GDPR Compliance in IoT Systems
Allegue, S., Rhahla, M., & Abdellatif, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45989-5_11 · Cited by 11
ABUDUREYIMU, Y., & Ogurlu, Y. (2021) · İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi · doi:10.46928/iticusbe.863505 · Cited by 11
Vgena, K., Kitsiou, A., Kalloniatis, C., & Gritzalis, S. (2022) · Future Internet · doi:10.3390/fi14090249 · Cited by 12
A decade of gender bias in machine translation
Savoldi, B., Bastings, J., Bentivogli, L., & Vanmassenhove, E. (2025) · Patterns · doi:10.1016/j.patter.2025.101257 · Cited by 17
“196”
Pietrantonio, F., Florczak, M., Kühn, S., Karberg, K., Leung, T. I., Said-Criado, I., Sikorski, S., Ruggeri, M., Signorini, A., Rosiello, F., et al. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1370555 · Cited by 12
Surveying federated learning approaches through a multi-criteria categorization
Caruccio, L., Cimino, G., Deufemia, V., Iuliano, G., & Stanzione, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16050-0 · Cited by 11
Examining How GDPR Challenges Emerging Technologies
El-Gazzar, R. F., & Stendal, K. (2020) · Journal of Information Policy · doi:10.5325/jinfopoli.10.2020.0237 · Cited by 10
Bringing privacy control back to citizens
Stach, C., Gritti, C., & Mitschang, B. (2020) · ACM Symposium on Applied Computing · doi:10.1145/3341105.3375754 · Cited by 8
“In particular Privacy by Design (Article 25) necessitates user-friendly tools to oversee and control how IoT platforms process their data and share it with IoT applications [24].”
BackgroundTelemedicine in internal medicine: A statement by the European Federation of Internal Medicine.
Pietrantonio, F., Sebastian, K., Kärberg, K., et al. (2023) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2023.02.021 · Cited by 8
Combrink, H., Marivate, V. N., & Masikisiki, B. (2023) · Education Sciences · doi:10.3390/educsci13050438 · Cited by 7
Applications of Machine Learning in Cyber Forensics
Rajendiran, K., Kannan, K., & Yu, Y. (2020) · Advances in digital crime, forensics, and cyber terrorism book series · doi:10.4018/978-1-7998-4900-1.ch002 · Cited by 8
Kang Woo, C. H. O., Jeong, B.-G., & Shin, S. U. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021ngp0006 · Cited by 8
“In other words, the strength of the security required to protect these data is the same as that required to protect personal information [3]–[5].”
BackgroundSecurity Management Suitable for Lifecycle of Personal Information in Multi-User IoT Environment
Lee, Y., & Lee, G. Y. (2021) · Sensors · doi:10.3390/s21227592 · Cited by 8
Data, resilience, and identity in the digital age
Davies, P., Fritzsche, A., Parry, G., & Wood, Z. (2023) · Strategic Change · doi:10.1002/jsc.2560 · Cited by 8
Kosek‐Szott, K., Szott, S., Ciężobka, W., et al. (2025) · Computer Communications · doi:10.1016/j.comcom.2025.108400 · Cited by 7
Extending an LGPD Compliance Inspection Checklist to Assess IoT Solutions: An Initial Proposal
Pereira, I., Mendes, J., Viana, D., et al. (2022) · doi:10.5753/cbsoft_estendido.2022.226679 · Cited by 7
“Central and State Governments in many countries have enacted laws and regulations to promote personal data protection [Wachter 2018].”
BackgroundThe Ethics of Surveillance in Times of Emergency
Macnish, K., & Henschke, A. (2023) · doi:10.1093/oso/9780192864918.001.0001 · Cited by 7
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
