Privacy Preservation in Data Streams: An Efficient Query Algorithm Based on CGP

Abstract

There are serious problems of privacy protection for continuous spatial data streams in the edge computing scenario. Traditional Concentrated Geo-Privacy (CGP) mechanisms and the existing adaptive methods both have a utility-efficiency bottleneck. Due to high concurrency and resource limitations in the environment, the current model has a large computational cost, and local spatial pruning methods often overlook the risk of cardinality leakage to deduce the true density. In response to the aforementioned weaknesses, this paper puts forward a lightweight collaborative geographic privacy preservation mechanism for dynamic data streams called Pruning-CGP. First, a physically separated query architecture will separate data ingestion from real-time queries to reduce computation. Second, in the zero-concentrated Differential Privacy (zCDP) setting, a discrete Gaussian mechanism is used for cardinality-preserving noise addition. In conjunction with the public prior, an adaptive distribution of the privacy budget is realised. Extensive physical stress tests with actual data have shown that the engineering feasibility of the proposed architecture has been realised. Under the above-mentioned privacy-equivalent constraints, the system has blocked side-channel cardinality leakage. Although it has a relatively low recall rate of about 31.5% and keeps the distance error at a practical hundred-meter level (e.g., 62 meters), it also achieves an extremely fast sub-millisecond response speed of 0.16 ms. Based on the above results, the Pruning-CGP mechanism can be applied to address the demand for a high-performance, low-resource algorithm in the context of edge computing.