# Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections

arXiv: https://arxiv.org/abs/2306.07884 · HTML: https://mlsanity.com/paper/2306.07884/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2023.

## Built on by (most cited first, 7 of 7)

- [Online Differentially Private Synthetic Data Generation](https://mlsanity.com/paper/2402.08012/) (2024)
- [An Algorithm for Streaming Differentially Private Data](https://mlsanity.com/paper/2401.14577/) (2024)
- [Differentially Private Synthetic Data Generation for Relational Databases](https://mlsanity.com/paper/2405.18670/) (2024)
- [Methods for generating and evaluating synthetic longitudinal patient data: a systematic review](https://mlsanity.com/paper/2309.12380/) (2023)
- [Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data](https://mlsanity.com/paper/2602.02766/) (2026)
- [Differentially Private Synthetic High-dimensional Tabular Stream](https://mlsanity.com/paper/2409.00322/) (2024)
- [Minimax optimal differentially private synthetic data for smooth queries](https://mlsanity.com/paper/2602.01607/) (2026)

## Abstract (arXiv)

Motivated by privacy concerns in long-term longitudinal studies in medical and social science research, we study the problem of continually releasing differentially private synthetic data from longitudinal data collections. We introduce a model where, in every time step, each individual reports a new data element, and the goal of the synthesizer is to incrementally update a synthetic dataset in a consistent way to capture a rich class of statistical properties. We give continual synthetic data generation algorithms that preserve two basic types of queries: fixed time window queries and cumulative time queries. We show nearly tight upper bounds on the error rates of these algorithms and demonstrate their empirical performance on realistically sized datasets from the U.S. Census Bureau's Survey of Income and Program Participation.