Privacy-Preserving Data Aggregation Mechanisms in Mobile Crowdsensing Driven by Edge Intelligence

Edge Intelligence (EI) empowers Mobile Crowdsensing (MCS) with real-time, distributed processing capabilities, but these advancements exacerbate long-standing privacy challenges. The strict requirements for low-latency computation on heterogeneous, resource-constrained edge nodes often conflict with the significant overhead imposed by traditional privacy-preserving techniques. Furthermore, distributed data flows and dynamic network conditions expand the attack surface, complicating risk containment. However, existing surveys do not examine privacy-preserving data aggregation through the lens of EI-specific constraints, a gap that this work aims to address. To this end, this paper systematically reviews recent privacy-preserving aggregation mechanisms from an EI-oriented perspective that accounts for real-time constraints, energy limitations, and decentralized cooperation. The survey examines emerging attack models and defense strategies associated with distributed collaboration and evaluates their implications for aggregation security in EI environments. Existing methods are categorized and assessed according to MCS system architecture and lifecycles, revealing limitations in applicability, scalability, and suitability under EI constraints. By integrating current techniques with experimental findings, this paper identifies open challenges and outlines promising directions for enhancing privacy protection in EI-driven MCS, offering both conceptual and analytical insights and practical guidance for future system design.