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In this work, we develop a privacy-by-design generative model forsynthesizing the activity diary of the travel population using state-of-artdeep learning approaches. This proposed approach extends literature onpopulation synthesis by contributing novel deep learning to the development andapplication of synthetic travel data while guaranteeing privacy protection formembers of the sample population on which the synthetic populations are based.First, we show a complete de-generalization of activity diaries to simulate thesocioeconomic features and longitudinal sequences of geographically andtemporally explicit activities. Second, we introduce a differential privacyapproach to control the level of resolution disclosing the uniqueness of surveyparticipants. Finally, we experiment using the Generative Adversarial Networks(GANs). We evaluate the statistical distributions, pairwise correlations andmeasure the level of privacy guaranteed on simulated datasets for varyingnoise. The results of the model show successes in simulating activity diariescomposed of multiple outputs including structured socio-economic features andsequential tour activities in a differentially private manner.

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