Mobile crowdsensing has emerged as a prominent paradigm that employs a multitude of intelligent mobile sensing devices to collect data across diverse environments. Recently, social-aware mobile crowdsensing has garnered substantial attention, prompting extensive research efforts. Several incentive mechanisms have been proposed to enhance system performance by exploiting social relationships among workers. However, while focusing on the role of social relationships, these approaches often overlook the significant impact of geographical factors on mobile crowdsensing systems. In this paper, we propose a novel incentive mechanism that maximizes platform utility by jointly considering both social relationships and geographical locations among workers. The proposed framework operates in two key phases: first, it computes a composite value that integrates workers’ skills, social relationships, and geographical positions. Subsequently, it selects optimal workers for sensing tasks based on this comprehensive evaluation. To further protect the privacy of workers’ bidding information within this incentive mechanism, we design a privacy-preserving bidding scheme that incorporates homomorphic encryption and digital signatures. Extensive simulation results demonstrate that our integrated solution not only optimizes platform revenue but also significantly enhances the security of task auctions within the system.