Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among existing privacy models, epsilon-differential privacy provides one of the strongest privacy guarantees and has no assumptions about an adversary's background knowledge. All the existing solutions that ensure epsilon-differential privacy handle the problem of disclosing relational and set-valued data in a privacy preserving manner separately. We developed an algorithm that considers both relational and set-valued data in differentially private disclosure of healthcare data.
Categories
Machine LearningLicense
Creative Commons Attribution LicenseFollow DiffGen
Other Useful Business Software
Gen AI apps are built with MongoDB Atlas
MongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of DiffGen!