Differential privacy for shared forecasts

This technical brief treats differential privacy for shared forecasts as a decision system: define its state, objective, feasible actions and evidence before comparing algorithms.

Practitioner lens

The strongest evaluation of differential privacy for shared forecasts compares decision quality with a transparent baseline, preserves time ordering and reports how performance varies by scenario.

First experiment

Write down the objective, constraints, uncertainty set and decision horizon before selecting a solver or model.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.