Privacy-preserving quadratic truth discovery based on Precision partitioning

Abstract

Truth discovery technology is widely used in the field of data collection in crowdsourcing; however, with the deepening of people’s privacy awareness, ordinary truth discovery can no longer meet the current user demand for privacy protection, and solving the privacy problem is one of the critical challenges of truth discovery. A number of works have been proposed as the truth discovery mechanism of differential privacy. However, the privacy budget allocation in the known differential privacy truth discovery mechanisms does not consider the data precision differences of multi-source datasets and the anomalous results of small-value data after aggregation. We propose a precision division based on a differential privacy two-layer truth discovery framework to ensure privacy security while considering data precision and can also ensure high-accuracy truth discovery. The main challenges of this paper are how to obtain highly accurate truth values in sparse data scenarios without exposing the original values to the cloud server when performing error correction of aggregated anomalies after noise addition, as well as to improve the precision of truth value estimation of perturbed streaming data based on the refinement of the privacy protection level based on the accuracy of the data. Specifically, we first formulate a data-sampling algorithm to get the data precision of different users and to sieve out the anomalous data and duplicate data to obtain the quality of user-uploaded data. Then, we formulate a new privacy budget allocation mechanism, which synthesizes the sampling situation during data preprocessing and fully considers the data precision to quantify user privacy and turn it into specific values. We provide a quadratic truth discovery mechanism based on a predictive interpolation algorithm when dealing with small-value data, ensuring the reliability of small data aggregation results. We demonstrate that our framework achieves differential privacy for user-supplied data while we conduct extensive experiments on three real-world datasets to prove the effectiveness of our system framework.