Topic Editors

Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
Dr. Safiullah Khan
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BX, UK

Privacy Challenges and Solutions in the Internet of Things

Abstract submission deadline
20 September 2027
Manuscript submission deadline
20 November 2027
Viewed by
6281

Topic Information

Dear Colleagues,

The Internet of Things (IoT) is a rapidly growing network of interconnected devices, such as sensors, wearables, vehicles, and appliances, embedded with software, sensors, and connectivity to collect and exchange data over the internet. These devices enable a variety of services such as real-time monitoring, smart decision-making, automation, and surveillance in various domains such as smart homes, healthcare, agriculture, smart cities, and industrial systems. Despite the numerous benefits of this seamless connectivity, IoT also introduces significant privacy and security challenges. IoT devices are often deployed in open environments with the least defense mechanisms, making them easy targets for attackers. Also, the lack of standardized security/privacy protocols across vendors leads to inconsistent protections. As IoT devices mostly collect sensitive personal data, such as health readings, location, spatio-temporal activities, and daily habits, they increase the risk of data breaches and unauthorized profiling. Improper handling of IoT data can lead to sensitive information disclosure, prediction of sensitive attributes, unauthorized profiling, revelation of preferences/habits, etc. Vulnerabilities in device firmware, which often go unpatched, can be exploited by attackers, while poorly designed encryption practices expose data to interception during transfer. Addressing these issues requires robust privacy-preserving methods, regular software updates, encrypted communications, lightweight privacy-preserving solutions that can operate on resource-constrained IoT devices, and adherence to privacy-by-design principles to ensure privacy-preserving and trustworthy IoT ecosystems. We invite researchers/practitioners to contribute novel methodologies/solutions for enhancing privacy and security in the IoT ecosystem from any perspective. This topic will attract high-quality papers on privacy and security solutions in the IoT ecosystem from leading experts in the field.

For this Topic, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Federated learning based methods for privacy enhancement in IoT ecosystems;
  2. Lightweight encryption methods for protecting IoT data;
  3. Differential privacy-based methods for IoT data security;
  4. Anonymization methods for optimizing privacy–utility trade-off in IoT data handling;
  5. Privacy protection methods for IoT data analytics;
  6. Statistical approaches for privacy protection in IoT data;
  7. Privacy of IoT data encompasses diverse modalities (e.g., spatio-temporal data, streaming data, probabilistic data, etc.);
  8. Privacy-preserving architectures for processing high-dimensional IoT data;
  9. IoT Data classification methods based on sensivity to ensure targeted privacy;
  10. Security of IoT data: advanced topics (e.g., LLM, NoSQL databases, structured datasets);
  11. Hybrid methods for privacy protection of IoT data;
  12. AI-based methods for security and privacy enhancement of IoT data;
  13. Privacy protection for multi-modality data stemming from IoT environments;
  14. Trade-off optimization in IoT data processing;
  15. Privacy protection in the lifecycle of AI-IoT applications;
  16. Hardware implementation for privacy/security enhancements of IoT data;
  17. Post-quantum cryptography for the IoT.

We look forward to receiving your contributions.

Dr. Abdul Majeed
Dr. Safiullah Khan
Topic Editors

Keywords

  • IoT data privacy
  • privacy-by-design methods
  • lightweight encryption
  • diverse data modalities
  • differential privacy
  • hybrid privacy methods
  • anonymization
  • blockchain
  • high-dimensional IoT data
  • LLM-powered solutions
  • AI/ML-based privacy solutions

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Electronics
electronics
2.9 7.0 2012 14.8 Days CHF 2400 Submit
Future Internet
futureinternet
4.6 10.0 2009 15 Days CHF 1800 Submit
Information
information
4.3 8.2 2010 18.7 Days CHF 1800 Submit
IoT
IoT
4.3 8.0 2020 24.2 Days CHF 1400 Submit
Journal of Sensor and Actuator Networks
jsan
4.8 11.3 2012 24.4 Days CHF 2000 Submit
Sensors
sensors
4.0 9.4 2001 17.8 Days CHF 2600 Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

  1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
  2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
  3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
  4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
  5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (2 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
14 pages, 601 KB  
Article
Automated Framework for Testing Random Number Generators for IoT Security Applications Using NIST SP 800-22
by Juan Castillo, Pere Aran Vila, Francisco Palacio, Blas Garrido, Sergi Hernández and Albert Cirera
IoT 2026, 7(1), 26; https://doi.org/10.3390/iot7010026 - 7 Mar 2026
Viewed by 1718
Abstract
The continuous expansion of the Internet of Things (IoT) has intensified the need to evaluate and guarantee the quality of entropy sources used in random number generation, an essential element in securing communications used in IoT ecosystems. This work presents an automated and [...] Read more.
The continuous expansion of the Internet of Things (IoT) has intensified the need to evaluate and guarantee the quality of entropy sources used in random number generation, an essential element in securing communications used in IoT ecosystems. This work presents an automated and web-based framework designed to execute and analyze the results of statistical tests defined in the NIST SP 800-22 standard, enabling systematic assessment of entropy sources and random numbers generators in IoT devices and environments. The proposed system integrates a Python-based backend built upon an optimized implementation of the original NIST suite, along with an intuitive web interface that facilitates configuration, monitoring, and parallel execution of tests through Representational State Transfer (REST) endpoints. Session management based on Redis ensures reliable and concurrent operation of multiple users or devices while maintaining isolation and data integrity. To demonstrate its applicability, an emulated IoT ecosystem was implemented in which multiple virtual devices periodically and asynchronously request real-time validation of their local random numbers generators. The obtained results confirm the system’s capability to detect deficiencies in pseudo random generators and validate true random number sources, highlighting its potential as a diagnostic and verification tool for distributed IoT security systems. The tool developed in this work is fully accessible to the public, allowing researchers, engineers, and practitioners to evaluate random number generators without requiring specialized hardware or proprietary software. Full article
Show Figures

Figure 1

23 pages, 1898 KB  
Article
A Container-Native IAM Framework for Secure Green Mobility: A Case Study with Keycloak and Kubernetes
by Alexandre Sousa, Frederico Branco, Arsénio Reis and Manuel J. C. S. Reis
Information 2025, 16(9), 802; https://doi.org/10.3390/info16090802 - 15 Sep 2025
Cited by 1 | Viewed by 1803
Abstract
The rapid adoption of green mobility solutions—such as electric-vehicle sharing and intelligent transportation systems—has accelerated the integration of Internet of Things (IoT) technologies, introducing complex security and performance challenges. While conceptual Identity and Access Management (IAM) frameworks exist, few are empirically validated for [...] Read more.
The rapid adoption of green mobility solutions—such as electric-vehicle sharing and intelligent transportation systems—has accelerated the integration of Internet of Things (IoT) technologies, introducing complex security and performance challenges. While conceptual Identity and Access Management (IAM) frameworks exist, few are empirically validated for the scale, heterogeneity, and real-time demands of modern mobility ecosystems. This work presents a data-backed, container-native reference architecture for secure and resilient Authentication, Authorization, and Accounting (AAA) in green mobility environments. The framework integrates Keycloak within a Kubernetes-orchestrated infrastructure and applies Zero Trust and defense-in-depth principles. Effectiveness is demonstrated through rigorous benchmarking across latency, throughput, memory footprint, and automated fault recovery. Compared to a monolithic baseline, the proposed architecture achieves over 300% higher throughput, 90% faster startup times, and 75% lower idle memory usage while enabling full service restoration in under one minute. This work establishes a validated deployment blueprint for IAM in IoT-driven transportation systems, offering a practical foundation for a secure and scalable mobility infrastructure. Full article
Show Figures

Figure 1

Back to TopTop