Local Differential Privacy for Belief Functions
This work addresses privacy concerns in belief function systems, which is an incremental contribution to the field of differential privacy.
The paper tackles the problem of defining local differential privacy for belief functions by proposing two new definitions based on Shafer's semantics and imprecise probabilities, and it provides a hypothesis testing framework while studying the trade-off between privacy and utility in discrete distribution estimation.
In this paper, we propose two new definitions of local differential privacy for belief functions. One is based on Shafer's semantics of randomly coded messages and the other from the perspective of imprecise probabilities. We show that such basic properties as composition and post-processing also hold for our new definitions. Moreover, we provide a hypothesis testing framework for these definitions and study the effect of "don't know" in the trade-off between privacy and utility in discrete distribution estimation.