A Privacy-Preserving Scheme With High Utility Over Data Streams in Mobile Crowdsensing

Both truth discovery and pattern analysis are effective methods for extracting valuable insights from data streams in mobile crowdsensing. However, existing privacy-preserving schemes either suffer from low data utility or provide high utility at the cost of weak privacy protection. To address this challenge, we introduce a robust privacy-preserving scheme that facilitates high-utility truth discovery and pattern analysis over mobile crowdsensing data streams. Concretely, we leverage the Square Wave mechanism, a randomized reporting technique, to perturb the data to prevent privacy breaches. To reduce the utility loss caused by perturbation, we design a budget allocation algorithm. This algorithm ensures that adjacent timestamps with approximate data share a perturbed value derived from their accumulated budgets. Furthermore, to facilitate robust pattern analysis, we propose a data splitting method that divides the perturbed data into two parts: one part records patterns randomly, while the other part recovers the perturbed values. Theoretical analysis confirms that our scheme satisfies $\omega $ -event $\epsilon $ -differential privacy level. Extensive experiments conducted on four real-world datasets demonstrate that our scheme outperforms existing schemes, delivering more accurate results for both truth discovery and pattern analysis under the same privacy constraints.