Portrait de Nishanth Chandran

Nishanth Chandran

Senior Principal Researcher

Nouvelles et reportages

EzPC provides secure AI model validation. In the diagram poses the following question: Is the accuracy of the AI model on the test dataset greater than 70%? First, an AI vendor provides model weights, and a modular compiler takes as input from the model weights the AI model structure written in ONNX code for ML inference. From this, it automatically generates MPC protocol code, which is then compiled into various MPC protocols. Additionally, a suite of highly performant cryptographic protocols securely compute complex ML functions on an organization’s test dataset. The MPC protocol outputs random bits, keeping the data from both parties secure.
Blog de recherche Microsoft

| Nishanth Chandran, Divya Gupta, Aseem Rastogi, et Rahul Sharma

From manufacturing and logistics to agriculture and transportation, the expansion of artificial intelligence (AI) in the last decade has revolutionized a multitude of industries—examples include enhancing predictive analytics on the manufacturing floor and making microclimate predictions so that farmers can…

Graphic shows framework of Privacy Preserving Machine Learning
Blog de recherche Microsoft

| Victor Ruehle, Robert Sim, Sergey Yekhanin, Nishanth Chandran, Melissa Chase, Daniel Jones, Kim Laine, Boris Köpf, Jaime Teevan, Jim Kleewein, et Saravan Rajmohan

Machine learning (ML) offers tremendous opportunities to increase productivity. However, ML systems are only as good as the quality of the data that informs the training of ML models. And training ML models requires a significant amount of data, more…