EzPC (Easy Secure Multi-party Computation)
뉴스 & 기능
Research Focus: Week of January 13, 2025
In this edition: Privacy enhancements for multiparty deep learning; using smaller, open-source models to provide relevance judgments; new tool uses AI, data to automate innovation and development; Yasuyuki Matsushita named IEEE 2025 Computer Society Fellow.
Research Focus: Week of October 7, 2024
Simplifying secure decision tree training; Improving accuracy of audio content detection; A novel neurosymbolic system for converting text to tables; New video series: AI for Business Transformation; TEE security protections for container workloads.
Research Focus: Week of July 17, 2023
RetroRanker mitigates frequency bias in predictions of retrosynthesis models; new algorithm beats PPO on language tasks; DER dataset aids grid planning; improved PPML balances privacy & accuracy across shared data; ASL Citizen boosts sign language modeling.
Those who have worked in the domain of data science know that developing an artificial intelligence (AI) model typically includes three stages at a high level: training, validation, and testing. When testing the accuracy of the model, there are usually…
EzPC: Increased data security in the AI model validation process
| Nishanth Chandran, Divya Gupta, Aseem Rastogi, 그리고 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…
Privacy Preserving Machine Learning: Maintaining confidentiality and preserving trust
| Victor Ruehle, Robert Sim, Sergey Yekhanin, Nishanth Chandran, Melissa Chase, Daniel Jones, Kim Laine, Boris Köpf, Jaime Teevan, Jim Kleewein, 그리고 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…