Certified Randomness from Quantum Supremacy
Explore quantum supremacy, certified randomness, and their implications in cryptography with Scott Aaronson's insightful presentation on cutting-edge quantum computing concepts.
Dark Data
Explore the concept of dark data, its retention, and processing, with a focus on personal data and legal compliance in data management.
Cryptographically Protected Database Search
Exploration of cryptographically protected database search systems, analyzing primitive operations, current implementations, potential attacks, and future directions for enhancing data security and functionality.
Exploiting Unintended Feature Leakage in Collaborative Learning - Congzheng Song
Explores security vulnerabilities in collaborative machine learning, demonstrating how model updates can leak sensitive information about training data and proposing inference attacks to exploit these weaknesses.
Wireguard - A Free, Open Source, Self Hosted, Cross Platform VPN Tunnel Built With Speed & Security
Learn to set up Wireguard VPN tunnel on servers, clients, and iOS devices for secure, fast, cross-platform connectivity using this step-by-step tutorial.
Inclusive Search and Recommendations
Explore inclusive AI in personalized discovery systems, focusing on fairness, diversity, and privacy. Learn strategies for reducing bias and implementing responsible AI in search and recommendation algorithms.
Face Recognition and Privacy: Balancing Technology and Personal Rights - 2019
Exploring face recognition technology, its applications, privacy implications, and global legislation. Dispels myths and discusses potential for privacy-preserving implementations of this identification method.
Trustworthy Machine Learning: Challenges and Frameworks
Exploring the security challenges in machine learning, this talk discusses strategies for building trustworthy AI systems by analyzing data influence, addressing vulnerabilities, and enhancing interpretability.
Callisto: A Cryptographic Approach to #MeToo
Innovative platform using cryptography to support sexual assault survivors, detect repeat offenders, and facilitate safe reporting while preserving privacy and legal privilege.
The Evolving Architecture of the Web and Its Impact on Security, Privacy, and Latency
Explore how changes in web architecture impact security, privacy, and latency. Examine evolving protocols and debate tradeoffs between complexity and user benefits.
The Edge of Developed Practice in Searching Encrypted Data
Exploring practical applications of searching encrypted data, focusing on structured encryption for efficient querying in high-performance distributed systems and its integration into modern databases.
Moderating End-to-End Encrypted Content - Balancing Privacy and Control
Exploring end-to-end encryption challenges, balancing privacy with content moderation needs. Proposes user-centric definitions and analyzes improvement strategies to maintain security while enhancing user experience.
Encrypted Messaging: Current Challenges and Future Implications
Expert panel explores the history, current state, and global implications of encryption debates, discussing potential solutions and mitigations for preserving digital privacy and security.
Privacy-Preserving Algorithms for Decentralised Collaborative Learning - Dr Aurélien Bellet
Explore privacy-preserving algorithms for decentralized collaborative learning, focusing on large-scale machine learning, distributed methods, and privacy protection in connected device data analysis.
Federated Heavy Hitters Discovery with Differential Privacy
Explore federated learning, differential privacy, and heavy hitters discovery in this talk by Peter Kairouz. Learn about multiparty computation, open research, and practical applications in data analysis.
Jelani Nelson- Forty Years of Frequent Items
Explore forty years of frequent items algorithms, from classic approaches to modern techniques, including turnstile streaming, heavy hitters, and local differential privacy.
How to Allow Deep Learning on Your Data Without Revealing Your Data
Exploring secure deep learning on private data without revealing it, discussing InstaHide and TextHide methods, their applications in Federated Learning, and addressing potential security concerns.
Trustworthy Deep Learning
Explore robust, accurate, efficient, and private deep learning with theoretical guarantees, drawing insights from differential equations for trustworthy AI applications.
Premonitions of Public Data for Private ML
Explore how public data pre-training enhances private machine learning, examining its impact on accuracy, zero-shot learning, and the future of privacy-preserving AI techniques.
Differentially Private Sampling from Distributions
Explore techniques for private sampling from distributions, focusing on accuracy, privacy guarantees, and applications in statistical data analysis.
Fundamental Trade-Offs in FL-FA - Sparsity - DP - Communication Constraints
Explore trade-offs in federated learning, focusing on sparsity, differential privacy, and communication constraints. Gain insights into balancing these factors for effective machine learning.