Introduction to Secure Multi-Party Computation
Explore secure multi-party computation techniques for joint calculations on private data without revealing inputs. Learn about homomorphic encryption, garbled circuits, and secret sharing.
Federated Learning with Formal User-Level Differential Privacy Guarantees
Explore federated learning with user-level differential privacy guarantees. Discover scalable algorithms for industrial workloads in data privacy and machine learning.
Confidential Computing - The Minimum Developers Should Know
Explore Confidential Computing essentials: technologies, importance, and ecosystem. Learn about FHE, SMPC, TEEs, and more. Gain insights into industry collaborations and community engagement.
Practical Cryptographic Solutions for Secure Inference and Private Benchmarking
Explore practical cryptographic solutions for secure inference and private benchmarking in machine learning, presented by Nishanth Chandran at PPML 2024.
Samplable Anonymous Aggregation for Private Federated Data Analysis
Kunal Talwar explores samplable anonymous aggregation for private federated data analysis, discussing innovative approaches to privacy-preserving machine learning in distributed settings.
MD-ML: Super Fast Privacy-Preserving Machine Learning for Malicious Security with Dishonest Majority
Explore groundbreaking advances in privacy-preserving machine learning with MD-ML framework, offering enhanced security and efficiency for multi-party computation in neural network inference and training.
Protecting WebAssembly Workloads with Confidential Computing and Trusted Execution Environments
Explore how to secure WebAssembly workloads using Confidential Computing and Trusted Execution Environments, focusing on WASI implementation, privacy-preserving AI, and practical applications in cloud/edge computing.