88 citations · 115 across the 4 of their papers we have counts for
5 papers
Privadome: Protecting Citizen Privacy from Delivery Drones
Gokulnath Pillai, Eikansh Gupta, Ajith Suresh +2
As e-commerce companies begin to consider using delivery drones for customer fulfillment, there are growing concerns around citizen privacy. Drones are equipped with cameras, and t…
BLAZE: Blazing Fast Privacy-Preserving Machine Learning
Arpita Patra, Ajith Suresh
Machine learning tools have illustrated their potential in many significant sectors such as healthcare and finance, to aide in deriving useful inferences. The sensitive and confide…
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
Nishat Koti, Mahak Pancholi, Arpita Patra +1
Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an emergent field of research. Recen…
ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction
Harsh Chaudhari, Ashish Choudhury, Arpita Patra +1
The concrete efficiency of secure computation has been the focus of many recent works. In this work, we present concretely-efficient protocols for secure -party computation (3PC…
Fast Actively Secure OT Extension for Short Secrets
Arpita Patra, Pratik Sarkar, Ajith Suresh
Oblivious Transfer (OT) is one of the most fundamental cryptographic primitives with wide-spread application in general secure multi-party computation (MPC) as well as in a number…