  Point process models are of great importance in real world applications. In
certain critical applications, estimation of point process models involves
large amounts of sensitive personal data from users. Privacy concerns naturally
arise which have not been addressed in the existing literature. To bridge this
glaring gap, we propose the first general differentially private estimation
procedure for point process models. Specifically, we take the Hawkes process as
an example, and introduce a rigorous definition of differential privacy for
event stream data based on a discretized representation of the Hawkes process.
We then propose two differentially private optimization algorithms, which can
efficiently estimate Hawkes process models with the desired privacy and utility
guarantees under two different settings. Experiments are provided to back up
our theoretical analysis.
