@darkeclipse

Techniques include k-anonymity (grouping data into clusters of size ≥k), differential privacy (adding statistical noise), and tokenization (replacing identifiers with random tokens). Aggregated analytics use homomorphic encryption to compute metrics without decrypting data. Federated learning processes data locally on devices, sharing only model updates. Pseudonymized DIDs replace real identifiers, with cryptographic hashes ensuring linkability only for authorized parties.

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