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Privacy in Internet of Things: From Principles to Technologies
Open accessAbstract
Ubiquitous deployment of low-cost smart devices and widespread use of high-speed wireless networks have led to the rapid development of the Internet of Things (IoT). IoT embraces countless physical objects that have not been involved in the traditional Internet and enables their interaction and cooperation to provide a wide range of IoT applications. Many services in the IoT may require a comprehensive understanding and analysis of data collected through a large number of physical devices that challenges both personal information privacy and the development of IoT. Information privacy in IoT is a broad and complex concept as its understanding and perception differ among individuals and its enforcement requires efforts from both legislation as well as technologies. In this paper, we review the state-of-the-art principles of privacy laws, the architectures for IoT and the representative privacy enhancing technologies (PETs). We analyze how legal principles can be supported through a careful implementation of PETs at various layers of a layered IoT architecture model to meet the privacy requirements of the individuals interacting with IoT systems. We demonstrate how privacy legislation maps to privacy principles which in turn drives the design of necessary PETs to be employed in the IoT architecture stack.
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“The personal information protection law is not specific and systematic, and the legal constraints are loose [114].”
From A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends (2024) · cites this paper as [114]
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Differential Privacy in New Settings
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A Survey of Middleware for Internet of Things
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Fair Information Practices: A Basic HistoryPreprint
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Oleshchuk, V. A. (2009) · International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology · doi:10.1109/wirelessvitae.2009.5172470 · Cited by 99
Children's Online Privacy Protection Act
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K-Anonymization as Spatial Indexing: Toward Scalable and Incremental Anonymization
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Lightweight Attribute-Based Encryption for the Internet of Things
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Security analysis of the constrained application protocol in the Internet of Things
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On the Feasibility of Attribute-Based Encryption on Smartphone Devices
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Anonymizing Streaming Data for Privacy Protection
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Goryczka, S., Xiong, L., & Sunderam, V. S. (2013) · EDBT/ICDT Workshops · doi:10.1145/2457317.2457343 · Cited by 66
Internet of Things Security and Privacy
Dabbagh, M., & Rayes, A. (2018) · Springer eBooks · doi:10.1007/978-3-319-99516-8_8 · Cited by 66
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The SENSEI Real World Internet Architecture
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UbiRoad: Semantic Middleware for Context-Aware Smart Road Environments
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A Reference Architecture for Improving Security and Privacy in Internet of Things Applications
Addo, I. D., Ahamed, S. I., Yau, S. S., & Buduru, A. B. (2014) · IEEE International Conference on Mobile Services · doi:10.1109/mobserv.2014.24 · Cited by 54
Internet of Things Security and Privacy
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K-anonymization as spatial indexing: toward scalable and incremental anonymization
Iwuchukwu, T., & Naughton, J. F. (2007) · Cited by 53
The Advanced Encryption Standard (AES)
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Security and Privacy Issues with Health Care Information Technology
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Privacy Law in the United States, the EU and Canada: The Allure of the Middle Ground
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Security and privacy grand challenges for the Internet of Things
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A Systemic Approach to Automate Privacy Policy Enforcement in Enterprises
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A TOR-based anonymous communication approach to secure smart home appliances
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Anonymous communication and its importance in social networking
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Securing Internet of Things with Lightweight IPsec
Raza, S., Chung, T., Duquennoy, S., Yazar, D., Voigt, T., & Roedig, U. (2010) · Diva portal (Dalarna University Library) · Cited by 31
Integrating Private Databases for Data Analysis
Wang, K., Fung, B. C. M., & Dong, G. (2005) · Lecture notes in computer science · doi:10.1007/11427995_14 · Cited by 30
On the challenges of building a web-based ubiquitous application platform
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The Privacy Coach: Supporting customer privacy in the Internet of ThingsPreprint
Broenink, G., Hoepman, J.-H., Hof, C. C. G. V. T., van Kranenburg, R., Smits, D., & Wisman, T. H. A. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1001.4459 · Cited by 27
Privacy Law in the United States, the EU and Canada: The Allure of the Middle Ground
Levin, A., & Nicholson, M. J. (2023) · doi:10.32920/22227775 · Cited by 26
A privacy protection policy combined with privacy homomorphism in the Internet of Things
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A Triple Space-Based Semantic Distributed Middleware for Internet of Things
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Privacy Injector — Automated Privacy Enforcement Through Aspects
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Towards Privacy-Preserving Data Sharing in Smart Environments
Hernández-Ramos, J. L., Bernabé, J. B., & Skármeta, A. (2014) · International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS) · doi:10.1109/imis.2014.44 · Cited by 12
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A Study on RAM Requirements of Various SHA-3 Candidates on Low-cost 8-bit CPUs.
Ideguchi, K., Owada, T., & Yoshida, H. (2009) · Cited by 11
Privacy-by-Design Framework for Assessing Internet of Things Applications and Platforms
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End-2-End privacy architecture for IoT
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Bormann, C. (2012) · Cited by 7
Flagged first, then by citations
5 of the 175 references couldn’t be matched to a known paper.
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Cited by
Sorted by citations
A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT
Sengupta, J., Ruj, S., & Das Bit, S. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102481 · Cited by 844
Khan, R., Kumar, P., Jayakody, D. N. K., & Liyanage, M. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2933899 · Cited by 625
Bhushan, B., Sahoo, C., Sinha, P., & Khamparia, A. (2020) · Wireless Networks · doi:10.1007/s11276-020-02445-6 · Cited by 284
Γιάνναρος, Α., Karras, A., Theodorakopoulos, L., Karras, C., Kranias, P., Schizas, N., Kalogeratos, G., & Tsolis, D. K. (2023) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp3030025 · Cited by 215
Joint Optimization of Offloading Utility and Privacy for Edge Computing Enabled IoT
Xu, X., He, C., Xu, Z., Qi, L., Wan, S., & Bhuiyan, M. Z. A. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2944007 · Cited by 190
Metadata
- Authors
- Li, C., & Palanisamy, B.
- Affiliations
- University of Pittsburgh (US)
- Venue
- IEEE Internet of Things Journal
- Published
- 2019-02
- Volume
- 6
- Issue
- 1
- Pages
- 488-505
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2885623796
- Cited by
- 130
- References
- 175
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 20 · 2023: 20 · 2022: 15 · 2021: 24 · 2020: 21 · 2019: 10
- FWCI
- 7.74
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- D-Scholarship@Pitt (University of Pittsburgh) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things, privacy, privacy-enhancing technologies, privacy law, privacy principles, IoT architecture, data privacy, data protection
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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