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

arXiv: https://arxiv.org/abs/2012.14574 · HTML: https://mlsanity.com/paper/2012.14574/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Choice modelling in the age of machine learning -- discussion paper](https://mlsanity.com/paper/2101.11948/) (2021)
- [Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review](https://mlsanity.com/paper/2306.00687/) (2023)
- [Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning](https://mlsanity.com/paper/2501.10221/) (2025)
- [A Comprehensive Evaluation Framework for Synthetic Trip Data Generation in Public Transport](https://mlsanity.com/paper/2510.24375/) (2025)
- [Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach](https://mlsanity.com/paper/2512.04223/) (2025)

## Abstract (arXiv)

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.