329 citations · 439 across the 15 of their papers we have counts for
16 papers
Metadata-Aware Multi-Prompt Reasoning for Zero-Shot Accident Understanding
Tarandeep Singh, Soumyanetra Pal, Soham Biswas +1
In this paper, we address the problem of zero-shot understanding of accidents from surveillance videos by identifying when an impact event occurs, what type of impact it is, and wh…
Who Gets Heard? Rethinking Fairness in AI for Music Systems
Atharva Mehta, Shivam Chauhan, Megha Sharma +5
In recent years, the music research community has examined risks of AI models for music, with generative AI models in particular, raised concerns about copyright, deepfakes, and tr…
Enterprise AI Must Enforce Participant-Aware Access Control
Shashank Shreedhar Bhatt, Tanmay Rajore, Khushboo Aggarwal +10
Large language models (LLMs) are increasingly deployed in enterprise settings where they interact with multiple users and are trained or fine-tuned on sensitive internal data. Whil…
TRUCE: Private Benchmarking to Prevent Contamination and Improve Comparative Evaluation of LLMs
Tanmay Rajore, Nishanth Chandran, Sunayana Sitaram +4
Benchmarking is the de-facto standard for evaluating LLMs, due to its speed, replicability and low cost. However, recent work has pointed out that the majority of the open source b…
TrustRate: A Decentralized Platform for Hijack-Resistant Anonymous Reviews
Rohit Dwivedula, Sriram Sridhar, Sambhav Satija +4
Reviews and ratings by users form a central component in several widely used products today (e.g., product reviews, ratings of online content, etc.), but today's platforms for mana…
Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains
Ananta Mukherjee, Peeyush Kumar, Boling Yang +2
This paper addresses privacy concerns in multi-agent reinforcement learning (MARL), specifically within the context of supply chains where individual strategic data must remain con…
Efficient ML Models for Practical Secure Inference
Vinod Ganesan, Anwesh Bhattacharya, Pratyush Kumar +3
ML-as-a-service continues to grow, and so does the need for very strong privacy guarantees. Secure inference has emerged as a potential solution, wherein cryptographic primitives a…
Telechain: Bridging Telecom Policy and Blockchain Practice
Sudheesh Singanamalla, Apurv Mehra, Nishanth Chandran +7
The use of blockchain in regulatory ecosystems is a promising approach to address challenges of compliance among mutually untrusted entities. In this work, we consider applications…
Multi-institution encrypted medical imaging AI validation without data sharing
Arjun Soin, Pratik Bhatu, Rohit Takhar +6
Adoption of artificial intelligence medical imaging applications is often impeded by barriers between healthcare systems and algorithm developers given that access to both private…
SIRNN: A Math Library for Secure RNN Inference
Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli +4
Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs) use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciproc…
Secure Medical Image Analysis with CrypTFlow
Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran +6
We present CRYPTFLOW, a system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build two compone…
Blockene: A High-throughput Blockchain Over Mobile Devices
Sambhav Satija, Apurv Mehra, Sudheesh Singanamalla +5
We introduce Blockene, a blockchain that reduces resource usage at member nodes by orders of magnitude, requiring only a smartphone to participate in block validation and consensus…
CrypTFlow2: Practical 2-Party Secure Inference
Deevashwer Rathee, Mayank Rathee, Nishant Kumar +4
We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both cor…
CrypTFlow: Secure TensorFlow Inference
Nishant Kumar, Mayank Rathee, Nishanth Chandran +3
We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we…
Position-Based Quantum Cryptography: Impossibility and Constructions
Harry Buhrman, Nishanth Chandran, Serge Fehr +4
In this work, we study position-based cryptography in the quantum setting. The aim is to use the geographical position of a party as its only credential. On the negative side, we s…
Position-Based Quantum Cryptography
Nishanth Chandran, Serge Fehr, Ran Gelles +2
This paper is replaced by arXiv:1009.2490. The new paper includes a general impossibility result and restricted possibility results, and it has two additional authors.