Kennedy Edemacu
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Lecturer
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Kennedy Edemacu's research focuses on privacy and security mechanisms within collaborative eHealth systems. His work investigates methods for protecting sensitive patient information, particularly in the context of mobile health and location-based services. Edemacu has explored differential privacy techniques and attribute-based encryption to enhance data sharing security. He has also studied multilevel privacy-preserving data sharing schemes and the challenges associated with attribute revocation in these systems.
His publications include survey articles on privacy-preserving mechanisms for location privacy and differential privacy in location-based services. Edemacu has also co-authored research on secure data sharing in collaborative eHealth using attribute revocation based on OBDD access structures and CP-ABE for efficient and secure data sharing. His collaborators include Xintao Wu, Karuna Bhaila, Alycia N. Carey, and Minh-Hao Van, all from the University of Arkansas at Fayetteville, with whom he has co-authored multiple publications.
Metrics
- h-index: 9
- Publications: 21
- Citations: 379
Positions
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University of Arkansas at Fayetteville 2023–presentComputer Science and Electrical Engineering ORCID
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Assistant Lecturer 2014–presentMuni University Computer and Information Science ORCID
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City University of New YorkComputer Science ORCID
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Assistant LecturerUniversity of Arkansas at Fayetteville ORCID
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Sangmyung University 2018–2021Computer Science ORCID
Selected Publications
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Fair In-Context Learning via Latent Concept Variables (2025)
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Privacy Preserving Prompt Engineering: A Survey (2025)
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DP-TabICL: In-Context Learning with Differentially Private Tabular Data (2024)
Collaboration Network
Top Collaborators
- Privacy Preserving Prompt Engineering: A Survey
- DP-TabICL: In-Context Learning with Differentially Private Tabular Data
- DP-TabICL: In-Context Learning with Differentially Private Tabular Data
- DP-TabICL: In-Context Learning with Differentially Private Tabular Data
- Fair In-Context Learning via Latent Concept Variables
- Fair In-Context Learning via Latent Concept Variables
- Fair In-Context Learning via Latent Concept Variables
- Fair In-Context Learning via Latent Concept Variables
- Fair In-Context Learning via Latent Concept Variables
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