Personal Reflections & Tech Insights

Blogs

Zero-Knowledge Proofs: Keeping AI Private

Zero-Knowledge Proofs offer a way to secure AI models and data without revealing sensitive information. It's a powerful concept for privacy, but comes with practical tradeoffs.

ML Supply Chain Security: Beyond the Model

Securing an ML system goes beyond the model itself. Protecting the entire supply chain, from data to deployment, is crucial for integrity and reliability.

The Catch with Computing on Encrypted Data

Homomorphic encryption lets you compute on data without decrypting it, a powerful idea for secure AI. But its complexity and performance hit are significant.

Differential Privacy for AI Model Deployment

Differential privacy is a technique used to protect sensitive data in machine learning models. This article explores advanced differential privacy techniques for AI model deployment in cloud-native environments.

Keeping Your AI Web Apps Safe

AI is awesome, but it brings new security worries to web apps. This guide breaks down how to protect your AI-powered sites from common threats, keeping your data and users safe.