A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis

TL;DR

This paper presents a novel deep learning generative model that synthesizes activity diaries with multiple outputs, ensuring privacy through differential privacy, and demonstrates its effectiveness with GANs on simulated travel data.

Contribution

It introduces a differentially private deep generative model for activity diary synthesis, extending population synthesis with privacy guarantees and multi-output capabilities.

Findings

Successfully simulates multi-output activity diaries with socioeconomic and sequential data.

Achieves privacy guarantees through differential privacy with varying noise levels.

Maintains statistical and correlation properties of real data in synthetic datasets.

Abstract

In this work, we develop a privacy-by-design generative model for synthesizing the activity diary of the travel population using state-of-art deep learning approaches. This proposed approach extends literature on population synthesis by contributing novel deep learning to the development and application of synthetic travel data while guaranteeing privacy protection for members of the sample population on which the synthetic populations are based. First, we show a complete de-generalization of activity diaries to simulate the socioeconomic features and longitudinal sequences of geographically and temporally explicit activities. Second, we introduce a differential privacy approach to control the level of resolution disclosing the uniqueness of survey participants. Finally, we experiment using the Generative Adversarial Networks (GANs). We evaluate the statistical distributions, pairwise correlations and measure the level of privacy guaranteed on simulated datasets for varying noise. The results of the model show successes in simulating activity diaries composed of multiple outputs including structured socio-economic features and sequential tour activities in a differentially private manner.