Sampling Based Fast Publishing Algorithm with Differential Privacy for Data Stream

Many cloud native database applications need to handle massive data streams. To analyze group trend information in these data streams in real time without compromising individual user privacy, these applications require the capability to quickly create differentially private histograms for the most recent dataset at any given moment. However, existing histogram publishing methods lack efficient data structures, making it difficult to rapidly extract key information to ensure real-time data usability. To address this issue, we deeply analyze the relationship between data sampling and privacy protection, and propose a sampling based fast publishing algorithm with differential privacy for data stream (SPF). SPF introduces an efficient data stream sampling sketch structure (EDS) for the first time, which samples and statistically estimates data within a sliding window and filters out unreasonable data, enabling rapid extraction of key information. Then, we demonstrate that the approximations output by the EDS structure are theoretically equivalent to adding differential privacy noise to the true values. Finally, to meet the privacy protection strength provided by the user while reflecting the true situation of the original data stream, an adaptive noise addition algorithm based on efficient data stream sampling is proposed. According to the relationship between the user-provided privacy protection strength and the privacy protection strength provided by the EDS structure, the algorithm adaptively generates the final publishable histogram through privacy allocation. Experiments show that compared with existing algorithms, SPF significantly reduces time and space overhead while maintaining the same data usability.