Massive amounts of personal data drive modern machine- learning pipelines, but that same data can also pose privacy risks. This study gathers and reorganizes scattered empirical evidence on privacy- preserving methods- such as differential privacy, federated optimization, secure aggregation, private transfer learning, and fully homomorphic encryption- into a practical strategy that practitioners can follow confidently. Instead of collecting new datasets, we review twelve peer- reviewed experiments from 2021 to 2025, re- analyze their metrics, and compare the results with regulatory thresholds from GDPR and the draft EU AI Act. The meta- analysis shows that keeping the privacy budget at two or less maintains macro- F 1 losses under three percentage points across vision, speech, and clinical tasks. However, energy costs increase by a median factor of 2.1. 1. Interestingly, speech- command recognition under DP- SGD became more stable, likely by reducing overfitting. Based on these findings, we introduce a tiered decision matrix: high- sensitivity data require DP- SGD with adaptive clipping; geographically fragmented datasets benefit from federated learning coupled with threshold aggr
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