In modern edge scenarios, the security of sensor networks plays a pivotal role in ensuring the safe communication of IoT data. Therefore, deploying encryption algorithms among edge devices is of paramount importance. However, most existing research predominantly emphasizes the efficiency of data transmission while often overlooking critical aspects of data integrity and confidentiality. The communication between sensor nodes and from sensors to end nodes necessitates additional robust security measures.This paper presents an enhanced approach to the RSA and AES algorithms, tailored for deployment on low-energy IoT devices commonly found in edge environments. The improved AES algorithm transitions from a single-threaded encryption and decryption process to a three-threaded design, reducing the ten-round operation to seven rounds to enhance both speed and energy efficiency. The enhanced RSA algorithm replaces the traditional dual-prime system with a triple-prime system, increasing the number of primes while reducing the bit length of each prime. This modification optimizes computational efficiency and strengthens security.Furthermore, we investigate the feasibility of integrating the RSA and AES algorithms and propose a novel RSA-AES hybrid algorithm MRA. This fusion approach leverages the strengths of both algorithms, encrypting the AES key using the improved RSA algorithm to bolster data security while maintaining efficient transmission.Performance evaluations of the RSA-AES hybrid algorithm demonstrate its exceptional capability in securing data transmission within edge scenarios,and the security of this hybrid algorithm is analyzed. The experimental results highlight that this algorithm not only ensures robust data security but also achieves high transmission efficiency, offering a reliable and practical solution for secure communication in edge environments.

As cloud computing proliferates, outsourced data faces severe security threats, yet existing searchable encryption (SE) schemes rely on classical hardness assumptions, centralized trust authorities, and static access control, leaving critical gaps in quantum resistance, single-point-of-failure prevention, and dynamic permission management. To address these limitations, we propose BL-ABSE, a blockchain-enhanced, lattice-based attribute-based searchable encryption framework. BL-ABSE employs the Ring Learning With Errors (RLWE) problem as its security foundation and applies the Number Theoretic Transform (NTT) to reduce polynomial multiplication from $O\left(n^2\right)$ to $O(nlogn)$. To eliminate single-point trust risks, the framework further integrates a $(t,n)$ threshold key protocol across an edge-node consortium governed by Practical Byzantine Fault Tolerance (PBFT) consensus. A smart-contract-maintained on-chain revocation list enables permission withdrawal via a single blockchain transaction without re-encryption. Experimental evaluation demonstrates that commitment generation requires approximately 23 ms at $n=1024$, search latency scales linearly at roughly 29 µs per record, and revocation completes in approximately 2 s regardless of system scale. Formal security proofs under the quantum polynomial-time (QPT) adversary model reduce six security properties—index indistinguishability, query privacy, threshold key security, Byzantine fault tolerance, audit immutability, and revocation immediacy—to the hardness of RLWE and the Short Integer Solution (SIS) problems. To the best of our knowledge, BL-ABSE is the first framework to simultaneously achieve post-quantum security, attribute-based access control, decentralized key management, instant revocation, and immutable auditing within a single unified framework. We further conduct threshold parameter verification, end-to-end revocation latency decomposition, blockchain throughput stress testing, search-pattern leakage quantification, and communication/storage overhead analysis, providing a comprehensive evaluation of both performance and security trade-offs. We explicitly characterize the search-pattern leakage inherent in the deterministic commitment design as a correctness–privacy trade-off and discuss mitigation directions.