privacypreservingAnsätze
Privacy-preserving methods aim to enable data-driven progress while protecting individuals’ personal information. They combine technical techniques, governance, and policy to reduce the risk of exposure or misuse of data, often by limiting what data is collected, how it is processed, and who can access it.
Differential privacy is a central approach, introducing carefully calibrated random noise to query results or public
Federated learning and edge computing enable model training or analysis without transferring raw data to a
Cryptographic techniques provide strong guarantees for multi-party collaboration. Secure multiparty computation allows parties to compute a
Practical considerations include balancing data utility with privacy, managing privacy budgets, and adhering to legal frameworks