Block ModShift: Model Privacy via Dynamic Designed Shifts

The problem of model privacy against an eavesdropper (Eve) in a distributed learning environment is investigated. The solution is found via evaluating the Fisher Information Matrix (FIM) for the model learning problem for Eve. Through a model shift design process, the eavesdropper’s FIM can be driven to singularity, yielding a provably hard estimation problem for Eve. Both a one-shot and multi-shot solution are designed. These two approaches require the sharing of a modest amount of information