Privacy-preserving heterogeneous health data sharing

Privacy-preserving heterogeneous health data sharing

OBJECTIVE Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among existing privacy models, ε-differential privacy provides one of the strongest privacy guarantees and makes no assumptions about an adversary's background knowledge. All existing solutions that ensure ε-differential privacy handle the problem of disclosing rel...

Privacy-preserving Data Mining, Sharing and Publishing

In the constantly developing world, e-health systems hold great promise for improving global access to healthcare services. Current significant technological visions of innovation in healthcare systems identify an approach to join different technological sectors and the need for technological platforms as well. These are: standardized electronic health records (eHR), aggregated public health da...

Privacy-preserving Sanitization in Data Sharing

PRIVACY-PRESERVING SANITIZATION IN DATA SHARING

Privacy Preserving Information Sharing

DPSynthesizer: Differentially Private Data Synthesizer for Privacy Preserving Data Sharing

Differential privacy has recently emerged in private statistical data release as one of the strongest privacy guarantees. Releasing synthetic data that mimic original data with Differential privacy provides a promising way for privacy preserving data sharing and analytics while providing a rigorous privacy guarantee. However, to this date there is no open-source tools that allow users to genera...