Abstract—Sparse Mobile CrowdSensing is an efficient data collection paradigm that recruits participants to gather data from partial spatiotemporal regions and leverages inherent correlations among these data to infer the remaining uncollected data. However, enabling accurate inference requires participants to upload sensitive spatiotemporal information, which poses significant privacy leakage risks. Traditional methods address these risks by obfuscating the uploaded spatial data, but this often compromises inference accuracy.