Spatial crowdsourcing (SC) has recently emerged as a new crowdsourcing service paradigm, where workers move physically to designated locations to perform tasks. Most SC systems perform task assignment based on the spatial proximity between task locations and worker locations. Under such a strategy, workers can only perform tasks near them, which may result in low social welfare (i.e., the total profit of the platform and workers). In contrast, the newly emerging strategy of competitive task assignment (CTA) stimulates workers to compete for their preferred tasks, allowing optimization of the overall profit of SC systems. Among others, one novel CTA setting is competitive detour tasking, which allows workers to compete for tasks that need them to make detours from their original travel paths. However, it requires collecting each worker’s bidding profile which may expose private information. In light of this, in this article, we design, implement, and evaluate PrivCO, a new system framework enabling privacy-preserving competitive detour tasking services in SC. PrivCO delicately bridges state-of-the-art competitive detour tasking algorithms with lightweight cryptography, providing strong protections for workers’ bidding profiles. Extensive experiments over real-world datasets demonstrate that while offering strong security guarantees, PrivCO achieves social welfare comparable to the plaintext domain.

Mobile crowdsensing (MCS) has rapidly emerged as a popular paradigm for sensory data collection and benefited various location-based services and applications like road monitoring, smart transportation, and environmental monitoring. In practice, there often exist data-missing regions in the target sensing area, due to factors like limited budget, large area size, and scarcity of participants. This poses a demand for data recovery, which is commonly done based on the compressive sensing (CS) technique. However, CS-based data recovery requires access to sensory data tagged with locations, raising critical concerns on participants’ location privacy. While a plethora of location privacy techniques exist, most of them breach the data correlation inherently required by CS-based data recovery. Meanwhile, existing works mostly focus on protecting locations and overlook sensory data which may also indirectly lead to location leakages. In this paper, we propose SecDR, a new system design supporting secure, efficient, and accurate data recovery for location-based MCS applications. SecDR protects both locations and sensory data, and is built from a delicate synergy of CS-based data recovery and lightweight cryptography techniques. Extensive evaluations demonstrate that SecDR achieves promising performance and, even with stronger security guarantees, outperforms the state-of-the-art, with accuracy close to the plaintext domain.