The research background focuses on the core demands of secure and efficient transmission in the era of data sharing, revealing the three major challenges currently faced: economic value loss caused by cross organizational data silos, single point failure and privacy leakage risks in centralized platforms, and performance bottlenecks in large-scale node scenarios of blockchain. The research method innovatively proposes two technological paths: a blockchain dynamic sharding model based on trust management, which quantifies trust timeliness through spatiotemporal decay factors, achieves real-time sharding reassembly and load balancing, and solves the problem of traditional sharding security and performance imbalance; The revocable attribute based encryption scheme based on the blockchain adopts a private key/pre decryption key separation architecture and a computing outsourcing protocol to reduce computing overhead while ensuring revocation efficiency and adapt to edge computing scenarios. The research results have verified the superiority of the proposed scheme: experiments have shown that the system throughput significantly increases and latency decreases at the user scale; Effectively resist malicious node attacks and reduce the probability of failure through distributed ledger and trust management mechanisms; Compared to traditional attribute based encryption, key generation and encryption time overhead are reduced. The research conclusion emphasizes that this automated operation approach achieves full chain collaborative optimization through multidimensional technological innovation, supports the scalability and robustness of cloud data platforms in complex scenarios, and provides a secure and efficient technological paradigm for data sharing. Its value continues to be highlighted over time, supporting the development of enterprises, society, and government in multiple fields.

Weiyao Ma. Automated Operation Approach for Scalable Cloud Data Platform. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 131-139. https://doi.org/10.38007/IJBDIT.2026.070116.

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