Innovative Research Award
Sriven Srilakshmi Pulkaram
California State University, Dominguez Hills
| Sriven Srilakshmi Pulkaram |
| Affiliation |
California State University, Dominguez Hills |
| Country |
United States |
| Scopus ID |
60211143700 |
| Documents |
3 |
| Citations |
4 |
| h-index |
1 |
| Subject Area |
CyberSecurity |
| Event |
Global CSE Awards |
| Google Scholar |
FLw_CQwAAAAJ |
Sriven Srilakshmi Pulkaram is a researcher affiliated with California State University, Dominguez Hills, whose scholarly work focuses on cybersecurity, privacy-preserving machine learning, healthcare Internet of Things (IoT), federated learning, homomorphic encryption, and secure data aggregation. Her published contributions address emerging challenges in secure distributed intelligence and privacy-aware healthcare computing environments.[1]
Abstract
This article summarizes the academic profile, research accomplishments, publication record, and scholarly impact of Sriven Srilakshmi Pulkaram. Her research addresses cybersecurity and privacy challenges in healthcare IoT systems through federated learning, encrypted aggregation, and edge-assisted computing architectures designed to improve secure and scalable distributed intelligence.[1]
Keywords
Cybersecurity, Federated Learning, Healthcare IoT, Homomorphic Encryption, Privacy Preservation, Edge Computing, Secure Aggregation, Wearable Devices, Distributed Intelligence, Data Security.
Introduction
The increasing adoption of connected healthcare technologies has intensified concerns regarding privacy, security, and trustworthy data sharing. Sriven Srilakshmi Pulkaram’s research explores advanced cybersecurity mechanisms for healthcare IoT environments, emphasizing federated learning, encrypted computation, and privacy-preserving communication frameworks that support secure and efficient distributed machine learning systems.[1][2]
Research Profile
As a researcher in computer science and cybersecurity, Pulkaram focuses on privacy-enhancing technologies for distributed healthcare applications. Her work combines federated learning, homomorphic encryption, edge computing, and secure networking principles to address confidentiality, scalability, latency, and data protection requirements within modern digital healthcare infrastructures.[1][2]
Research Contributions
Her contributions include the development of privacy-aware federated learning frameworks, encrypted aggregation techniques, and edge-assisted architectures that enhance healthcare IoT security. These studies investigate practical methods for reducing communication overhead while maintaining confidentiality, model accuracy, scalability, and compliance with evolving privacy expectations in sensitive environments.[1][2][3]
Publications
The publication record includes research on encrypted federated learning for wearable healthcare systems, homomorphic encryption in health IoT networks, and privacy-preserving in-network aggregation approaches. These works collectively examine secure machine learning deployment, low-latency communication strategies, and efficient protection mechanisms for healthcare-related distributed data processing.[1][2][3]
Research Impact
The research contributes to the growing body of knowledge surrounding secure artificial intelligence and healthcare cybersecurity. By addressing privacy, latency, and scalability challenges simultaneously, these studies support the advancement of practical frameworks that may facilitate trustworthy deployment of distributed intelligence across healthcare and IoT ecosystems.[1][2][3]
Award Suitability
The Innovative Research Award recognizes researchers whose work demonstrates originality and relevance. Pulkaram’s investigations into privacy-preserving healthcare computing, encrypted machine learning, and cybersecurity-driven solutions align with the objectives of innovation-oriented recognition programs by addressing significant technical challenges through emerging computational methodologies.[1][2]
Conclusion
Sriven Srilakshmi Pulkaram has established an emerging research profile focused on cybersecurity and privacy-preserving healthcare technologies. Her scholarly contributions demonstrate engagement with contemporary challenges involving federated learning, encrypted computation, and secure IoT systems, supporting ongoing advancements in trustworthy and scalable digital healthcare environments.[1][2][3]
External Links
References
- Khan, H., Kavati, R., Pulkaram, S. S., & Jalooli, A. (2025). End-to-end privacy-aware federated learning for wearable health devices via encrypted aggregation in programmable networks. Sensors, 25(22), 7023.
https://www.mdpi.com/1424-8220/25/22/7023
- Pulkaram, S. S., et al. (2025). Securing Federated Learning in Health IoT with Edge-Assisted Homomorphic Encryption. IEEE Conference Proceedings.
https://ieeexplore.ieee.org/abstract/document/11393779
- Pulkaram, S. S., et al. (2025). Efficient Privacy-Preserving In-Network Data Aggregation for Low-Latency Healthcare IoT. IEEE Conference Proceedings.
https://ieeexplore.ieee.org/abstract/document/11393722
- Elsevier. (n.d.). Scopus author details: Sriven Srilakshmi Pulkaram, Author ID 60211143700. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=60211143700