The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
IoT Privacy and Security: Challenges and Solutions
Open accessAbstract
Privacy and security are among the significant challenges of the Internet of Things (IoT). Improper device updates, lack of efficient and robust security protocols, user unawareness, and famous active device monitoring are among the challenges that IoT is facing. In this work, we are exploring the background of IoT systems and security measures, and identifying (a) different security and privacy issues, (b) approaches used to secure the components of IoT-based environments and systems, (c) existing security solutions, and (d) the best privacy models necessary and suitable for different layers of IoT driven applications. In this work, we proposed a new IoT layered model: generic and stretched with the privacy and security components and layers identification. The proposed cloud/edge supported IoT system is implemented and evaluated. The lower layer represented by the IoT nodes generated from the Amazon Web Service (AWS) as Virtual Machines. The middle layer (edge) implemented as a Raspberry Pi 4 hardware kit with support of the Greengrass Edge Environment in AWS. We used the cloud-enabled IoT environment in AWS to implement the top layer (the cloud). The security protocols and critical management sessions were between each of these layers to ensure the privacy of the users’ information. We implemented security certificates to allow data transfer between the layers of the proposed cloud/edge enabled IoT model. Not only is the proposed system model eliminating possible security vulnerabilities, but it also can be used along with the best security techniques to countermeasure the cybersecurity threats facing each one of the layers; cloud, edge, and IoT.
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“The constrained resources of IoT devices render them susceptible to both physical and cyber threats.”
From Privacy-preserving security of IoT networks: A comparative analysis of methods and applications (2025) · cites this paper as [14]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“transparency and accountability deficits among IoT service providers and users raise ethical and legal concerns”
From Privacy-preserving security of IoT networks: A comparative analysis of methods and applications (2025) · cites this paper as [14]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“insufficient awareness and education among IoT stakeholders heighten the risk of privacy violations and security breaches”
From Privacy-preserving security of IoT networks: A comparative analysis of methods and applications (2025) · cites this paper as [14]
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“Among the primary challenges is the large-scale and heterogeneous composition of IoT networks, which complicates management and oversight.”
From Privacy-preserving security of IoT networks: A comparative analysis of methods and applications (2025) · cites this paper as [14]
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“A lack of standardized protocols exacerbates interoperability issues”
From Privacy-preserving security of IoT networks: A comparative analysis of methods and applications (2025) · cites this paper as [14]
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“Recent research indicated how a Fitbit device was subjected to data injection attacks, battery drain hacks and denial-of-service attacks [130].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [130]
In the citing paper
“Through these advanced countermeasures, it has been observed that IoMT can significantly improve the efficiency of the healthcare system by ensuring the safety of patients [14], [15].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [14]
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Citations
Cites
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Self-citationSecurity techniques for intelligent spam sensing and anomaly detection in online social platforms
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Research works printed but not linked: 1
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Cited by
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Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366
Metadata
- Authors
- Tawalbeh, L., Muheidat, F., Tawalbeh, M., & Quwaider, M.
- Affiliations
- Texas A&M University – San Antonio (US)
- California State University, San Bernardino (US)
- Jordan University of Science and Technology (JO)
- Corresponding author
- Tawalbeh, Lo’ai
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-06-15
- Volume
- 10
- Issue
- 12
- Pages
- 4102
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3034175800
- Cited by
- 764
- References
- 27
- Citations by year
- 2026: 53 · 2025: 150 · 2024: 173 · 2023: 160 · 2022: 121 · 2021: 96 · 2020: 10
- FWCI
- 45.47
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- Digital Commons @ Texas A&M University-San Antonio (Texas A&M University-San Antonio) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Expanded Chancellor Research Initiative grant (CRI) awarded to Texas A&M University- San Antonio, TX, USA. (2019)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- Internet of Things security, IoT privacy, Raspberry Pi, security certificates, security protocols, user awareness, IoT vulnerabilities
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