An Algorithm for Streaming Differentially Private Data
This work addresses the challenge of maintaining privacy guarantees in real-time data streams, which is crucial for applications like IoT and online analytics, though it appears incremental by extending offline methods to streaming contexts.
The authors tackled the problem of applying differential privacy to streaming data, which previously either violated privacy or had poor utility, by developing an algorithm for generating differentially private synthetic streaming data, especially for spatial datasets, and demonstrated its utility on real-world and simulated datasets.
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.