Differential Privacy for Symbolic Systems with Application to Markov Chains. (arXiv:2202.03325v2 [cs.CR] UPDATED)

Data-driven systems are gathering increasing amounts of data from users, and
sensitive user data requires privacy protections. In some cases, the data
gathered is non-numerical or symbolic, and conventional approaches to privacy,
e.g., adding noise, do not apply, though such systems still require privacy
protections. Accordingly, we present a novel differential privacy framework for
protecting trajectories generated by symbolic systems. These trajectories can
be represented as words or strings over a finite alphabet. We develop new
differential privacy mechanisms that …

application differential privacy privacy systems