<p>Location-based services provide significant benefits to mobile users, yet they simultaneously introduce substantial privacy risks. Attackers can exploit continuous query sequences to localize and track individuals through comparative analysis which is a problem that remains understudied and urgently requires effective preservation mechanisms. Existing trajectory privacy methods predominantly rely on centralized architectures involving trusted third parties, which leads to scalability issues during peak loads and introduces single points of failure vulnerable to concentrated attacks. To address these limitations, this study proposes a collaborative trajectory privacy framework for continuous queries. The framework first acquires services from h-hop neighbors and identifies candidate users that satisfy trajectory similarity constraints, then performs trajectory obfuscation through cloaked-region construction using these candidate sets. Furthermore, recognizing the limitations of existing incentive mechanisms in peer-to-peer information exchange contexts, specifically that monetary pricing introduces substantial overhead and reciprocity-based approaches impose significant storage requirements, this paper introduces a socially optimal rating protocol. The protocol comprises a recommended strategy and a rating update rule designed to incentivize collaboration and reward high-quality service provision, with service quality evaluated based on users’ historical contributions to the anonymization process. Experimental evaluation confirms the framework’s efficacy in neighbor service acquisition and trajectory privacy preservation, as measured by fake query rates and anonymization success metrics.</p>