Differentially Private Estimation of Hawkes Process

TL;DR

This paper introduces the first general method for differentially private estimation of Hawkes process models, addressing privacy concerns in sensitive point process data applications.

Contribution

It provides a rigorous differential privacy framework for Hawkes processes and proposes two efficient algorithms with privacy-utility guarantees.

Findings

Algorithms achieve desired privacy and utility trade-offs.

Experimental results validate theoretical guarantees.

First to address privacy in point process model estimation.

Abstract

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.