Ph.D. Candidate, Information Systems
Kelvin Echenim is a Ph.D. Candidate at the University of Maryland, Baltimore County (UMBC) advised by Dr. Karuna Joshi. His general research interest is in Internet of Things (IoT) Wearables, Cloud Computing and Networking.
Kelvin has a Bachelors in Electrical/Electronic Engineering and an MBA from Nigeria. He has worked for over 10 years as a Field Services Engineer and Manager at Globacom Limited .
Publications
- Kelvin Echenim and Karuna P. Joshi, “IoT-Reg: A Comprehensive Knowledge Graph for Real-Time IoT Data Privacy Compliance“, IEEE Big Data 2023, 3rd Workshop on Knowledge Graphs and Big Data, December 2023.
- Kelvin Uzoma Echenim, Lavanya Elluri and Karuna P Joshi, “Ensuring Privacy Policy Compliance of Wearables with IoT Regulations“, IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (IEEE TPS 2023), November 2023
Kelvin successfully defended his Ph.D. Proposal on 6 February 2025.
Proposal: A Real-Time Framework for IoT Data Privacy Compliance: Knowledge Graphs and LLM Integration
Committee: Dr. Karuna P Joshi (Chair), Dr. Tera Reynolds, Dr. James Foulds, Dr. Patricia Ordoñez, Dr. Lavanya Elluri (TAMUCT)
Abstract: The Internet of Things (IoT) drives continuous data generation across diverse domains, from consumer wearables to industrial systems, while posing significant challenges to privacy compliance. Overlapping regulatory mandates demand adaptive, scalable, and real-time solutions. Previous approaches, constrained by static compliance checks and reliance on isolated standards, have proven insufficient for dynamic IoT environments. This research addresses these gaps by developing a multiregulatory, real-time compliance framework that integrates IoT data and regulatory rules within a knowledge graph (KG), enabling robust reasoning and accessibility. The research evolves across three phases. The first phase leveraged NISTIR 8228 to annotate privacy policy elements for wearable IoT devices, semi-automating compliance checks, and demonstrating structured compliance mapping. The second phase scaled this effort with IoT-Reg, a comprehensive KG unifying GDPR, HIPAA, and NISTIR 8228 under a shared data lifecycle model. This innovation enabled the systematic detection of regulatory violations across diverse contexts. The third phase integrated large language models (LLMs) with the KG to automate compliance checks and translate natural language queries into SPARQL queries, incorporating deontic logic and significantly enhancing user accessibility. Preliminary validations showed the framework’s ability to detect violations accurately and provide actionable recommendations, affirming its feasibility for real-world applications. While effective, the system identifies challenges, including the need to scale partial reasoning, manage continuous sensor data, and extend support to multimodal data streams. Future work will address these gaps by incorporating streaming data pipelines, automated regulatory updates, and advanced LLM orchestration to adapt to evolving regulations in real time. This research aims to deliver a domain-flexible, scalable, and user-friendly IoT compliance framework that ensures robust data privacy and governance in an increasingly interconnected IoT landscape.
Kelvin successfully cleared his Ph.D. Comprehensive Exams in March 2024.