Confidential Computing: A History
Here, we provide a deep dive into Confidential Computing, how it can protect data privacy, and where it comes from?
Discover Confidential Computing by coding your own KMS inside an enclave
Discover confidential computing with our tutorials. Fill the knowledge gap, become proficient in secure enclaves, and craft applications with their strengths. Join us to become a Confidential Computing wizard! Dive into our content and start your journey today.
Mithril Security Raised €1.2 Million to Protect LLM Users' Data
With BlindBox, you can use Large Language Models without any intermediary or model owner seeing the data sent to the models. This type of solution is critical today, as the newfound ease-of-use of generative AI (GPT4, MidJourney, GitHub Copilot…) is already revolutionizing the tech industry.
Rust: How We Built a Privacy Framework for Data Science
We could have built our privacy framework BastionLab in any language - Python, for example, which is data science’s beloved. But we chose Rust because of its efficiency and security features. Here are the reasons why we loved doing so, but also some challenges we encountered along the way.
Data Science: The Short Guide to Privacy Technologies
If you’re wondering what the benefits and weaknesses of differential privacy, confidential computing, federated learning, etc are, and how they can be combined to improve artificial intelligence and data privacy, you’ve come to the right place.
How Python Data Science Libraries Can Be Hijacked (and What You Can Do About It)
Hackers can easily hijack the data science libraries you use every day and get full access to the datasets you are working with. Data owners need tools to prevent it from happening.
Jupyter Notebooks Are Not Made for Sensitive Data Science Collaboration
When collaborating remotely on sensitive data, their usually amazing interactivity and flexibility need safeguards, or whole datasets can be extracted in a few lines of code.