Accuracy-Enabling Differential Privacy-Preserving Truth Discovery
Man Zhang, Xinghua Li, Yinbin Miao, Bin Luo, Siqi Ma, Robert Huijie Deng · IEEE Transactions on Dependable and Secure Computing · 2025
Perturbation-based privacy-preserving truth discovery requires the Service Provider (SP) to calculate the truthful aggregation result from perturbed data of the Data Sources (DSs), which inevitably damages the aggregation accuracy due to perturbation noise added in the data. Thus, the existing works attempt to relieve the perturbation errors by reducing noise amounts or adjusting aggregation weights of DSs. However, the former sacrifices DSs' privacy preservation and the latter has the limited accuracy recovery performance. Aiming at it, we propose an accuracy-enabling differential privacy-preserving truth discovery consisting of an independence-guaranteed data perturbation module and a progressive-private noise elimination module. Specifically, in the first module, SP generates mass of noises following DS's desired perturbation parameters and DS privately obtains one of noise based on private information retrieval. Meanwhile, to realize the perturbation's traceability, SP preserves the ciphertext of DSs' acquired noises, assisting the following noise elimination. In the second module, SP first removes his preserved DS's encrypted noise from perturbed truth according to homomorphic encryption, and then requires DS to decrypt this cleaned truth. The above two processes are progressively and iteratively implemented until all DSs have been involved. Theoretical analysis shows that our scheme can protect DSs' raw data privacy in both truth discovery process and noise elimination process. Extensive experiments using the real-world dataset demonstrate that our scheme can effectively eliminate more than 90% of the perturbation noise effects on the truth discovery accuracy.