Parth Nagar
PhD researcher in privacy-preserving ML and cryptography
Puttaparthi, Andhra Pradesh, India
Joined February 2026
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Summary
Focused researcher in privacy-preserving machine learning and cryptography: Parth's stated research concentrates on privacy-enhancing technologies (PETs) — especially Fully Homomorphic Encryption (FHE), machine learning on encrypted data, and federated learning — applied to secure analytics. researchgate+1
Work and projects centered on privacy-preserving anomaly detection: Parth has contributed to research applying federated learning and homomorphic encryption to anomaly detection in IoT and related distributed/private learning frameworks. researchgate+2
Academic progression at Sri Sathya Sai Institute of Higher Learning: Parth completed BSc (Hons) and MSc at SSSIHL and is affiliated there as a doctoral researcher / research scholar, indicating an academic trajectory within the same institution. researchgate+2
Engaged in the academic and conference circuit: Parth maintains researcher identifiers and profiles (ResearchGate, ORCID, LinkedIn) and has work accepted/published in conferences and compiled venues related to security, privacy, and distributed learning. orcid+2
Work
Education
Projects
Writing
A Quorum-Based Privacy-Preserving Distributed Learning Framework for Anomaly Detection
January 1, 2025Book chapter / conference paper presenting a quorum-based privacy-preserving distributed learning framework aimed at anomaly detection.
A Fully Privacy-Preserving Solution for Anomaly Detection in IoT using Federated Learning and Homomorphic Encryption
November 1, 2023Paper describing a privacy-preserving approach to anomaly detection in IoT environments using federated learning combined with homomorphic encryption techniques.
Anomaly Detection Through Unsupervised Federated Learning
Work on anomaly detection using unsupervised federated learning methods.
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