# Continuous Release of Data Streams under both Centralized and Local Differential Privacy

arXiv: https://arxiv.org/abs/2005.11753 · HTML: https://mlsanity.com/paper/2005.11753/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2020.

## Built on by (most cited first, 13 of 13)

- [Graph Unlearning](https://mlsanity.com/paper/2103.14991/) (2021)
- [Constant matters: Fine-grained Complexity of Differentially Private Continual Observation](https://mlsanity.com/paper/2202.11205/) (2022)
- [FACE-AUDITOR: Data Auditing in Facial Recognition Systems](https://mlsanity.com/paper/2304.02782/) (2023)
- [DPMLBench: Holistic Evaluation of Differentially Private Machine Learning](https://mlsanity.com/paper/2305.05900/) (2023)
- [ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2309.03081/) (2023)
- [DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training](https://mlsanity.com/paper/2403.02571/) (2024)
- [On the Privacy Risks of Cell-Based NAS Architectures](https://mlsanity.com/paper/2209.01688/) (2022)
- [An Algorithm for Streaming Differentially Private Data](https://mlsanity.com/paper/2401.14577/) (2024)
- [Local Differential Privacy for Smart Meter Data Sharing](https://mlsanity.com/paper/2311.04544/) (2023)
- [Differentially Private Synthetic High-dimensional Tabular Stream](https://mlsanity.com/paper/2409.00322/) (2024)
- [Prediction with Expert Advice under Local Differential Privacy](https://mlsanity.com/paper/2512.06971/) (2025)
- [DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2605.17432/) (2026)
- [ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledge](https://mlsanity.com/paper/2608.05737/) (2026)

## Abstract (arXiv)

In this paper, we study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value can be quite large; thus it is necessary to estimate a threshold so that numbers above it are truncated to reduce the amount of noise that is required to all the data. The estimation must be done based on the data in a private fashion. We develop such a method that uses the Exponential Mechanism with a quality function that approximates well the utility goal while maintaining a low sensitivity. Given the threshold, we then propose a novel online hierarchical method and several post-processing techniques. Building on these ideas, we formalize the steps into a framework for private publishing of stream data. Our framework consists of three components: a threshold optimizer that privately estimates the threshold, a perturber that adds calibrated noises to the stream, and a smoother that improves the result using post-processing. Within our framework, we design an algorithm satisfying the more stringent setting of DP called local DP (LDP). To our knowledge, this is the first LDP algorithm for publishing streaming data. Using four real-world datasets, we demonstrate that our mechanism outperforms the state-of-the-art by a factor of 6-10 orders of magnitude in terms of utility (measured by the mean squared error of answering a random range query).