Privacy-Preserving Machine Learning: Challenges and Opportunities - Invited Talk
Explore privacy-preserving machine learning with Sewoong Oh's invited talk at PPML 2023, covering cutting-edge techniques and advancements in protecting data privacy during ML processes.
Multi-Party Computation - PKC 2023 Session
Explore cutting-edge developments in Multi-Party Computation (MPC) through expert presentations and discussions at the PKC 2023 conference session.
FedLess - Secure and Scalable Serverless Federated Learning
Explore serverless federated learning with FedLess, addressing scalability and security challenges in collaborative model training across distributed clients using cloud platforms.
Information Theoretic Security - From Classical to Quantum
Explore classical and quantum information theoretic security with Prof. El Gamal, covering key concepts and advancements in cybersecurity and digital trust.
Homomorphic Encryption - CHES 2023
Explore cutting-edge advancements in homomorphic encryption at CHES 2023, featuring expert insights and the latest research in secure computation techniques.
5 Questions to Ask About Your Data for Choosing the Right Tool
Discover key questions to ask about your data's shape, usage, privacy, sensitivity, and volume to choose the right management tools and make informed decisions.
Secure Multi-Party Computation and Secret Sharing - Session 1 and 2
Explore the fundamentals of Multi-Party Computation and Secret Sharing techniques in cryptography, including key concepts, applications, and recent advancements in the field.
Multi-party Computation I - Session 109
Explore the fundamentals and applications of Multi-party Computation, a cryptographic protocol enabling secure collaborative computations while preserving data privacy.
Multiparty Computation and Garbled Circuits - Session 204
Explore secure multiparty computation and garbled circuits, advanced cryptographic techniques for privacy-preserving collaborative computations and secure function evaluation.
Zero Knowledge, Property-Preserving Hashing and ORAM
Explore advanced cryptographic concepts: Zero Knowledge proofs, Property-Preserving Hashing, and Oblivious RAM (ORAM). Gain insights into cutting-edge techniques for secure data storage and privacy-preserving computations.
Fully Homomorphic Encryption and Indistinguishability Obfuscation - Session 7
Explore fully homomorphic encryption (FHE) and its applications in input/output operations, focusing on advanced cryptographic techniques and their practical implementations.
Homomorphic Encryption Advances - Asiacrypt 2022 Session
Explore cutting-edge advancements in homomorphic encryption, a cryptographic technique enabling secure computations on encrypted data, presented by leading experts at Asiacrypt 2022.
Multi-Party Computation - Session 2
Explore advanced concepts in multi-party computation at TCC 2023, featuring cutting-edge research presentations and discussions on secure distributed computing protocols.
Multi-Party Computation for General Functionalities and Functional Encryption - Session 2
Explore advanced cryptographic techniques in multi-party computation and functional encryption, focusing on cutting-edge research and applications in secure data processing and privacy-preserving computations.
Functional Encryption - Session 1
Explore advanced cryptographic techniques and latest research in functional encryption, presented by leading experts at a major international cryptology conference.
Fundamentals of Privacy-Preserving Federated Learning
Explore privacy-preserving techniques in federated learning, focusing on data protection and collaborative model training across distributed datasets.
OpenFL: A Federated Learning Framework for Secure Collaborative Model Training
Explore OpenFL, a Python framework for federated learning that enables collaborative model training without sharing sensitive data. Learn its features, applications, and security benefits.
Homomorphic Encryption in the Open Source World
Explore homomorphic encryption's potential to revolutionize data privacy and security in open source environments, enabling secure data analysis and multi-party sharing without compromising confidentiality.
Federated AI in Future Digital Banking
Explore how WeBank leverages federated AI and open-source machine learning to revolutionize digital banking, addressing data privacy and regulatory challenges while enhancing customer experience.
Learning from Multiple Data Sources
Explore multi-source data learning strategies and their implications for machine learning algorithms, focusing on challenges and potential solutions in diverse data environments.
The Ethical Algorithm - Designing Socially Aware AI
Exploring ethical challenges in AI and machine learning, including data privacy, algorithmic bias, and decision-making transparency. Discusses recent research on socially responsible algorithm design.