Differential Privacy in Applied Analytics: Choosing Epsilon When Nobody Can Interpret It
DOI:
https://doi.org/10.5281/zenodo.22657265Keywords:
Differential Privacy in Applied Analytics; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
Differential Privacy in Applied Analytics is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for differential privacy in applied
analytics, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has converged
and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We conclude
that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance
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