Integrity verification scheme for distributed dynamic data in service ecosystems

Big data distributed storage provides solid data support for various service ecosystem services. The cloud computing platform is the key infrastructure to realize the management of big data distributed storage. To cope with increasingly complex network threats and data protection requirements, distributed storage systems often require a higher level of information-theoretic security assurance. Among them, how to realize data security audit and ensure data integrity and reliability is the core key technology that must be addressed in the field of cloud computing distributed storage. Existing cloud computing outsourced dynamic data audit schemes mainly rely on the security technology of computational complexity and still have such problems as insufficient security and poor availability, so it is difficult to directly apply or effectively extend them to distributed storage systems with requirements for information-theoretic security. In order to address this challenge, this paper proposes a lightweight algebraic remote data audit methodology, which explores an orthogonal authentication technique for the linear subspace generated from cloud-stored data vectors. This approach offers a novel application for algebraic coding in the context of distributed dynamic cloud storage auditing. Different from the existing dynamic audit mechanism, the proposed scheme does not rely on any authentication data structure, which ensures the real-time update and integrity audit of outsourced dynamic storage data. Experimental analysis demonstrates that the proposed scheme is capable of resisting forgery or replay attacks and achieving the objective of distributed information-theoretic security auditing. Compared with existing similar schemes, the proposed scheme involves lower storage overhead and less computation in the process of dynamic data updating.