Data has become a critical driver of innovation in artificial intelligence and the evolution of 6G technologies. The explosive growth of data volume accelerates the convergence of cloud and edge computing, while simultaneously posing heightened challenges to data security and privacy. The emerging cloud-edge-device collaborative paradigm enables dynamic and large-scale data circulation across heterogeneous entities, exposing systems to complex threats such as malicious edge nodes and eavesdropping over untrusted communication channels. In response to these issues, we propose EdgeGuard, a secure and decentralized framework for cloud-edge-device data circulation. EdgeGuard is specifically designed for highly dynamic environments and ensures robust data confidentiality, integrity, traceability, and resilience against both malicious external attackers and compromised edge servers. To underpin its cryptographic foundation, we develop two core primitives. Specifically, we introduce an Aggregatable Publicly Verifiable Secret Sharing (APVSS) scheme that enables efficient sharing of field elements while supporting aggregation and public verifiability. Furthermore, we construct AggDKG, a distributed key generation (DKG) protocol. AggDKG achieves public verifiability and bias resistance with an expected total communication cost of${\mathcal {O}}(\kappa n^{3})$effectively overcoming the scalability limitations inherent in traditional complaint-based protocols. Collectively, these components form a comprehensive framework that strengthens secure and efficient data circulation in cloud-edge-device systems. Experimental data show that AggDKG delivers clear performance gains: across all tested scales, its total running time is only about 8%–65% of that of the DKG of Gurkan et al., and at$n=256$, it reduces per-node runtime by approximately 27% compared with the DKG of Gennaro et al. These results highlight EdgeGuard's superior scalability, lower latency, and stronger Byzantine resilience for secure large-scale deployments.