A flexible approach to distributed data anonymization
We propose a flexible approach for de-identifying distributed biomedical datal. Horizontal and vertical data distribution are handled in a consistent manner. Our method supports a broad spectrum of anonymization methods and privacy criteria. Supported algorithms include optimal methods, heuristics and clustering algorithms. Applicable criteria include k-anonymity, l-diversity, t-closeness and d-presence.
distribution anonymization privacy personal data protection commutative encryption secure multi-party computing smc