a privacy-preserving compression storage method for large trajectory data in road network

The prevalence of gps applications and other mobile devices has led to the accumulation of a large amount of trajectory data that contains valuable information for intelligent transportation, route planning, city computing etc. however, massive data not only brings new challenges to data storage and retrieval but also leads to serious privacy risks because of the abundant spatiotemporal information. in this paper, we propose a storage scheme that strikes a balance between the compression ratio and precision. we then introduce a road segment generalization method to address privacy issues stemming from sensitive places. next, we design a two-layer index mechanism to provide an effective retrieval. furthermore, a privacy preserving storage system pp-trajstore is implemented. it provides efficient storage based on a road segment compression scheme, preserves privacy by employing sensitive segment generalization technologies, and achieves rapid retrieval by a two-layer index strategy. finally, a realworld dataset is utilized to demonstrate the performance of pp-trajstore