Efficient privacy-preserving truth discovery based on homomorphic encryption and secret sharing

Truth discovery protocols are widely employed in distributed and uncertain environments to extract the most reliable information, playing a pivotal role in crowd-sensing systems. However, existing approaches face several challenges, including privacy leakage, high user interactivity, and excessive computational overhead, which limit their practical applicability. To address these issues, this paper proposes a privacy-preserving truth discovery protocol that combines homomorphic encryption and arithmetic secret sharing. The proposed scheme ensures data security through additive secret sharing and facilitates secure computations using Paillier homomorphic encryption, eliminating the need for additional user interactions. Experimental results show that the protocol not only effectively preserves privacy but also significantly reduces communication and computational overhead, making it more feasible for real-world applications.