MIDAS: Load-Balanced Metadata-Outsourcing ID-Embedded Data Auditing Scheme for Mobile Devices

With the rapid growth of mobile applications, large volumes of private data are continuously generated on mobile devices and increasingly outsourced to cloud storage services. Due to the limited computational and energy resources of mobile devices, enabling efficient and secure periodic auditing of cloud stored data remains a challenging task. Existing cloud data auditing schemes, such as Provable Data Possession and Proof of Retrievability, require computation-intensive operations that are impractical for resource-constrained mobile environments. To address this challenge, we propose MIDAS, a mobile-friendly data auditing scheme that supports secure outsourcing of auditing metadata with balanced resource utilization. MIDAS adopts a two-phase auditing metadata structure, in which computation intensive operations are offloaded to edge nodes, while mobile devices only perform lightweight processing. In addition, identity information of edge nodes is embedded into the auditing metadata to support traceability and accountability. To further improve scalability, we design a resource scheduling model for Multi MU and Multi-EN scenarios and propose the proportional load balanced scheduling algorithm to achieve efficient and load balanced metadata generation. The security of MIDAS is rigorously proven by reducing it to the discrete logarithm problem in the RSA quadratic residue subgroup within the random oracle model. Experimental results in a real-world environment demonstrate that MIDAS significantly reduces computational overhead on mobile devices, maintains acceptable communication cost, and achieves superior scalability compared with existing schemes.