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.
Homomorphic Encryption with BFV, BGV and CKKS Using SEAL and C++/.NET
Explore advanced encryption techniques using SEAL library in C++/.NET, focusing on BFV, BGV, and CKKS schemes for secure data processing and analysis.
Automating Regulatory-Compliant Data Handling in Finance - A Case Study
Implementación de un modelo de gestión de datos conforme a regulaciones en el sector financiero, utilizando Databricks y AWS para automatizar el acceso y permisos basados en el principio de necesidad.
From Generic to Genius - Fine-Tuning LLMs with Precision and Speed
Discover how to fine-tune LLMs for your business needs, leveraging Databricks' Mosaic AI capabilities to create personalized models with improved speed, cost-efficiency, and data privacy.
Secure by Design: Strategies for LLM Adoption in Cloud-Native Environments
Explore strategies for secure LLM adoption in cloud-native environments, addressing data privacy, AI-agent architectures, and security hardening. Learn practical approaches and insights from real-world implementations.
Embracing AI in Education: A Path to Inclusive and Effective Learning
Explore AI's potential in education, addressing benefits, risks, and best practices for inclusive learning. Learn about generative AI, predictive tutoring, and strategies for responsible integration.
Consequences of an Insightful Algorithm
Explores ethical implications of data mining and algorithmic insights, examining unintended consequences and responsibilities in software development for privacy and consent.
Secret Sharing - Crypto 2024
Explore cutting-edge cryptographic techniques for secure data distribution and protection in this Crypto 2024 session chaired by Yuval Ishai.
Side Channels and Leakage in Cryptography
Explore cutting-edge research on cryptographic vulnerabilities and protection mechanisms in this expert-led session from a prestigious conference.
Fully Homomorphic Encryption - Crypto 2024
Explore cutting-edge advancements in Fully Homomorphic Encryption at Crypto 2024, featuring expert insights and the latest research in this crucial cryptographic field.
Cloud Cryptography
Explore cutting-edge cloud cryptography techniques and advancements presented at Crypto 2024, chaired by Giuseppe Persiano. Gain insights into the latest research and innovations in this critical field.
Denying Access to Production Systems - Principles and Practices
Explore principles and practices for maintaining a secure production environment without direct access, ensuring data privacy while preserving agility and release efficiency.
Writing JavaScript Books, Tech Education, and AI Startups - A Conversation with Kyle Simpson
Explore publishing, education, and tech innovation with Kyle Simpson as he shares insights on book writing, learning journeys, and his new AI venture prioritizing data ownership and privacy.
Federated Learning for Medical Image Analysis - Seminar 89
Explore cutting-edge federated learning applications in medical imaging with NVIDIA expert Holger Roth, focusing on collaborative AI development while maintaining data privacy and security.
Leave Home Safe App Security Assessment - The Good, the Bad, the Ugly
Explore security vulnerabilities in Hong Kong's LeaveHomeSafe app, from assessment techniques to disclosure challenges. Gain insights on securing contact tracing apps and improving data privacy.
Can You Actually Own Your Identity and Content on the Web?
Explore methods for reclaiming control over your online identity and content, ensuring data ownership and protection against platform shutdowns.
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models - Explained
Explore how tampering with pretrained models can compromise privacy, enabling attackers to reconstruct finetuning data. Learn about privacy backdoors in transformers and their implications for machine learning security.
Differentially Private Release of Spatio-Temporal Data Statistics
Explore strategies for releasing spatio-temporal data statistics with differential privacy, focusing on sample mean and variance calculations while minimizing reconstruction errors in urban data management.
Confidential Computing on Azure - Secure Container Deployment and Management with Contrast
Master secure container deployment in Azure through confidential computing, covering data protection, end-to-end encryption, cluster setup, and container management with Contrast integration.
Flipped Huber - A New Additive Noise Mechanism for Differential Privacy
Discover a novel additive noise mechanism for differential privacy that combines Laplace and Gaussian distributions, offering enhanced privacy protection while maintaining data accuracy in query responses.
Adversarial Machine Learning: Categories, Concepts, and Current Landscape
Explore adversarial machine learning vulnerabilities, including data subversion, evasion, and model revelation. Learn to identify potential threats and build robust defense strategies.