Differentially private synthesis of Spatial Point Processes
This paper proposes a method to generate synthetic data for spatial point patterns within the differential privacy (DP) framework. Specifically, we define a differentially private Poisson point synthesizer (PPS) and Cox point synthesizer (CPS) to generate synthetic point patterns with the concept of the $\alpha$-neighborhood that relaxes the original definition of DP. We present three example models to construct a differentially private PPS and CPS, providing sufficient conditions on their parameters to ensure the DP given a specified privacy budget. In addition, we demonstrate that the synthesizers can be applied to point patterns on the linear network. Simulation experiments demonstrate that the proposed approaches effectively maintain the privacy and utility of synthetic data.
Code (0)
등록된 구현이 없습니다.
Tasks
Point ProcessesSimilar Papers 제목 키워드 기반
Learning Differentially Private Mechanisms
Differential privacy is a formal, mathematical definition of data privacy that has gained traction in academia, industry, and government. The task of correctly constructing differentially private algorithms is non-trivia…
Program SynthesisDifferentially Private Synthetic Data: Applied Evaluations and Enhancements
Machine learning practitioners frequently seek to leverage the most informative available data, without violating the data owner's privacy, when building predictive models. Differentially private data synthesis protects …
BIG-bench Machine LearningDPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase
Differentially private synthetic data is a promising alternative for sensitive data release. Many differentially private generative models have been proposed in the literature. Unfortunately, they all suffer from the low…
Image GenerationDifferentially Private Learning of Hawkes Processes
Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data. While they have a rich history going back decades, some of their prope…
Differentially Private Regression and Classification with Sparse Gaussian Processes
A continuing challenge for machine learning is providing methods to perform computation on data while ensuring the data remains private. In this paper we build on the provable privacy guarantees of differential privacy w…
ClassificationGaussian ProcessesGeneral Classificationregression