Privacy-preserving task matching scheme for crowdsourcing

Crowdsourcing has become a crucial paradigm for task execution and data collection, with task matching serving as a fundamental application. Due to the potential untrustworthiness of crowdsourcing platforms, which may lead to the leakage of users’ private information, users are required to encrypt their data prior to uploading. To fulfill task matching while preserving privacy, the crowdsourcing platform employs encrypted spatial keyword queries to perform task matching of workers’ interests and locations. To achieve secure and efficient crowdsourcing task matching, a privacy-preserving spatial keyword similarity-based task matching (SKSTM) scheme for crowdsourcing was proposed. SKSTM encoded locations and keywords by using the Geohash algorithm and bitmap representation, transforming spatial keyword similarity search into inner product calculations. Security analysis and experimental results demonstrate that SKSTM outperforms state-of-the-art schemes in task matching while effectively preserving the privacy of both task requesters and workers.