most citedEnhancing Privacy in Face Analytics Using Fully Homomorphic Encryption

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2025

Shielding Latent Face Representations From Privacy Attacks

Arjun Ramesh Kaushik, Bharat Chandra Yalavarthi, Arun Ross +2

In today's data-driven analytics landscape, deep learning has become a powerful tool, with latent representations, known as embeddings, playing a central role in several applicatio…

cs.CL2024

Enhancing Authorship Attribution through Embedding Fusion: A Novel Approach with Masked and Encoder-Decoder Language Models

Arjun Ramesh Kaushik, Sunil Rufus R P, Nalini Ratha

The increasing prevalence of AI-generated content alongside human-written text underscores the need for reliable discrimination methods. To address this challenge, we propose a nov…

cs.CR2024

Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation

Arjun Ramesh Kaushik, Charanjit Jutla, Nalini Ratha

In safeguarding mission-critical systems, such as Unmanned Aerial Vehicles (UAVs), preserving the privacy of path trajectories during navigation is paramount. While the combination…

cs.CR2024

Enhancing Privacy and Security of Autonomous UAV Navigation

Vatsal Aggarwal, Arjun Ramesh Kaushik, Charanjit Jutla +1

Autonomous Unmanned Aerial Vehicles (UAVs) have become essential tools in defense, law enforcement, disaster response, and product delivery. These autonomous navigation systems req…

cs.CR20241 cited

Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption

Bharat Yalavarthi, Arjun Ramesh Kaushik, Arun Ross +2

Modern face recognition systems utilize deep neural networks to extract salient features from a face. These features denote embeddings in latent space and are often stored as templ…