Empirical tests show differential privacy keeps latency and throughput close to normal while SMC depends on network speed and FHE adds roughly 1000x response time, with accuracy losses up to 35% on complex models.
Privacy-enhanced data fusion for COVID-19 applications in intel- ligent Internet of medical Things [Journal Article]
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A Privacy-Preserving Machine Learning Framework for Edge Intelligence: An Empirical Analysis
Empirical tests show differential privacy keeps latency and throughput close to normal while SMC depends on network speed and FHE adds roughly 1000x response time, with accuracy losses up to 35% on complex models.