Differentially Private Estimation of Hawkes Process
This work addresses privacy concerns in critical applications like healthcare or finance where point process models use sensitive user data, representing an incremental advance by applying differential privacy to a new domain.
The authors tackled the problem of estimating Hawkes process models while protecting sensitive personal data in event streams, proposing the first differentially private estimation procedure with two efficient algorithms that achieve privacy and utility guarantees, as supported by experiments.
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.