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.

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Categories

Machine Learning

License

Creative Commons Attribution License

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Additional Project Details

Operating Systems

Windows

Intended Audience

Healthcare Industry

Programming Language

C++

Related Categories

C++ Machine Learning Software

Registered

2012-06-06