Optimized Lattice-Based Ciphertext Policy Attribute-Based Encryption for Quantum-Resistant Secure Data Sharing

The practical utilization of the conventional Ciphertext-Policy Attribute-Based Encryption (CP-ABE) systems in a vast cloud has the constraint of the high cost of computation and the high risk of quantum-powered adversaries. This research introduces a novel Hybrid Lattice-Based CP-ABE (HL-CP-ABE) architecture that is focused on the hardness assumptions of Ring Learning with Errors (RLWE) to provide the double security of quantum-safe and precise data sharing. The proposed hybrid encryption system employs post-quantum techniques that provide both confidentiality and access control, and it is completely based on AES for the data encryption and on the lattice-based CP-ABE technology for the AES session key wrapping. A multithreaded, chunk-based parallel processing technique is used to improve the encryption and decryption processes and, at the same time, limit the difficulties that come along with the performance of lattice-based cryptography. The entire system is implemented in Java and evaluated across file sizes from 100 to 800 MB under a fixed three-attribute access policy, achieving processing time reductions of up to 9.5× compared to serial lattice CP-ABE, with a mean speedup of approximately 8.2× across encryption and decryption. The results reveal that the design not only upholds security but also achieves scalability, reduced ciphertext growth, and the correct decryption of the whole data. This research study presents the groundwork for future optimization using GPU parallelization and dynamic attribute management, and at the same time, it puts forth a workable, effective, and quantum-secure method of attribute-based encryption that is specifically beneficial for electronic health record (EHR) systems and secure cloud data sharing.

@article{Purnima2026,
  title     = {Optimized Lattice-Based Ciphertext Policy Attribute-Based Encryption for Quantum-Resistant Secure Data Sharing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {7},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Purnima and Tejaswi Khanna and Deepak Kumar Verma},
  doi       = {10.14569/IJACSA.2026.0170707},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170707}
}