# An Algorithm for Streaming Differentially Private Data

arXiv: https://arxiv.org/abs/2401.14577 · HTML: https://mlsanity.com/paper/2401.14577/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Large Language Models Can Be Strong Differentially Private Learners](https://mlsanity.com/paper/2110.05679/) (2021)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Practical and Private (Deep) Learning without Sampling or Shuffling](https://mlsanity.com/paper/2103.00039/) (2021)
- [Collecting Telemetry Data Privately](https://mlsanity.com/paper/1712.01524/) (2017)
- [DP-CGAN: Differentially Private Synthetic Data and Label Generation](https://mlsanity.com/paper/2001.09700/) (2020)
- [Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe](https://mlsanity.com/paper/2210.14348/) (2022)
- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [Benchmarking Differentially Private Synthetic Data Generation Algorithms](https://mlsanity.com/paper/2112.09238/) (2021)
- [Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12](https://mlsanity.com/paper/1709.02753/) (2017)
- [PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions](https://mlsanity.com/paper/1601.03229/) (2016)
- [Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data](https://mlsanity.com/paper/1702.06270/) (2017)
- [Continuous Release of Data Streams under both Centralized and Local Differential Privacy](https://mlsanity.com/paper/2005.11753/) (2020)
- [HDMM: Optimizing error of high-dimensional statistical queries under differential privacy](https://mlsanity.com/paper/2106.12118/) (2021)
- [Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections](https://mlsanity.com/paper/2306.07884/) (2023)
- [Local Differential Privacy for Evolving Data](https://mlsanity.com/paper/1802.07128/) (2018)
- [Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear Regression](https://mlsanity.com/paper/2207.13289/) (2022)
- [Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation](https://mlsanity.com/paper/2306.06723/) (2023)

## Built on by (most cited first, 2 of 2)

- [Online Differentially Private Synthetic Data Generation](https://mlsanity.com/paper/2402.08012/) (2024)
- [Differentially Private Synthetic High-dimensional Tabular Stream](https://mlsanity.com/paper/2409.00322/) (2024)

## Abstract (arXiv)

Much of the research in differential privacy has focused on offline applications with the assumption that all data is available at once. When these algorithms are applied in practice to streams where data is collected over time, this either violates the privacy guarantees or results in poor utility. We derive an algorithm for differentially private synthetic streaming data generation, especially curated towards spatial datasets. Furthermore, we provide a general framework for online selective counting among a collection of queries which forms a basis for many tasks such as query answering and synthetic data generation. The utility of our algorithm is verified on both real-world and simulated datasets.