6 articles tagged with #data-security. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose a unified framework for AI security threats that categorizes attacks based on four directional interactions between data and models. The comprehensive taxonomy addresses vulnerabilities in foundation models through four categories: data-to-data, data-to-model, model-to-data, and model-to-model attacks.
MIT researchers have developed methods to test AI models used in clinical settings to prevent them from inadvertently revealing anonymized patient health data through memorization. This research addresses a critical privacy and security concern as healthcare AI systems become more prevalent.
OpenAI has announced new administrative and compliance tools for ChatGPT Enterprise, including API integrations, SCIM provisioning, and GPT controls. These features are designed to help organizations manage compliance programs, enhance data security, and scale user access management more effectively.
The article discusses techniques for performing sentiment analysis on encrypted data using homomorphic encryption. This approach allows analysis of sensitive data while maintaining privacy, potentially enabling new applications in finance and other sectors requiring data confidentiality.
The article discusses differentially private partition selection, a technique for securing private data at scale. This represents an advancement in privacy-preserving algorithms that can protect sensitive information while still allowing for data analysis and processing.
The article title suggests content about implementing automatic Personally Identifiable Information (PII) detection on a platform hub using Microsoft's Presidio tool. However, the article body appears to be empty or unavailable, preventing detailed analysis of the implementation details or implications.
