BMSQABSE: Design of a Bioinspired Model to Improve Security & QoS Performance for Blockchain-Powered Attribute-based Searchable Encryption Applications

Article Sidebar

PDF
Published: Jun 10, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.7114
Keywords:
Attribute, Encryption, Searchable, PSO, GWO, Stochastic, Security, QoS

Main Article Content

Roshni Bhave
Department of Computer Science and Engineering, YeshwantRao Chavan College of Engineering, Nagpur, India
Bhakti P. Thakre
Department of Information Security, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, India
Vijaya Kamble
Department of Computer Science and Engineering, GuruNanak Institute of Engineering & Technology, Nagpur, India
Purva Gogte
Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Dhananjay Bhagat
Department of Artificial Intelligence, G H Raisoni College of Engineering, Nagpur, India

Abstract

Attribute-based searchable encryption (ABSE) is a sub-field of security models that allow intensive searching capabilities for cloud-based shared storage applications. ABSE Models require higher computational power, which limits their application to high-performance computing devices. Moreover, ABSE uses linear secret sharing scheme (LSSS), which requires larger storage when compared with traditional encryption models. To reduce computational complexity, and optimize storage cost, various researchers have proposed use of Machine Learning Models (MLMs), that assist in identification & removal of storage & computational redundancies. But most of these models use static reconfiguration, thus cannot be applied to large-scale deployments. To overcome this limitation, a novel combination of Grey Wolf Optimization (GWO) with Particle Swarm Optimization (PSO) model to improve Security & QoS performance for Blockchain-powered Attribute-based Searchable Encryption deployments is proposed in this text. The proposed model augments ABSE parameters to reduce its complexity and improve QoS performance under different real-time user request scenarios. It intelligently selects cyclic source groups with prime order & generator values to create bilinear maps that are used for ABSE operations. The PSO Model assists in generation of initial cyclic population, and verifies its security levels, QoS levels, and deployment costs under multiple real-time cloud scenarios. Based on this initial analysis, the GWO Model continuously tunes ABSE parameters in order to achieve better QoS & security performance levels via stochastic operations. The proposed BMSQABSE model was tested under different cloud configurations, and its performance was evaluated for healthcare deployments. Based on this evaluation, it was observed that the proposed model achieved 8.3% lower delay, with 4.9% lower energy consumption, 14.5% lower storage requirements when compared with standard ABSE models. It was able to mitigate Distributed Denial of Service (DDoS), Masquerading, Finney, and Sybil attacks, which assists in deploying the proposed model for QoS-aware highly secure deployments.

Article Details

How to Cite
Bhave, R. ., Thakre, B. P. ., Kamble, V. ., Gogte, P. ., & Bhagat, D. . (2023). BMSQABSE: Design of a Bioinspired Model to Improve Security & QoS Performance for Blockchain-Powered Attribute-based Searchable Encryption Applications. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 527–535. https://doi.org/10.17762/ijritcc.v11i5s.7114
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
Section
Articles

References

L. Zhang, J. Su and Y. Mu, "Outsourcing Attributed-Based Ranked Searchable Encryption With Revocation for Cloud Storage," in IEEE Access, vol. 8, pp. 104344-104356, 2020, doi: 10.1109/ACCESS.2020.3000049.

S. Liu, J. Yu, Y. Xiao, Z. Wan, S. Wang and B. Yan, "BC-SABE: Blockchain-Aided Searchable Attribute-Based Encryption for Cloud-IoT," in IEEE Internet of Things Journal, vol. 7, no. 9, pp. 7851-7867, Sept. 2020, doi: 10.1109/JIOT.2020.2993231.

S. Wang, L. Yao, J. Chen and Y. Zhang, "KS-ABESwET: A Keyword Searchable Attribute-Based Encryption Scheme With Equality Test in the Internet of Things," in IEEE Access, vol. 7, pp. 80675-80696, 2019, doi: 10.1109/ACCESS.2019.2922646.

S. Wang, S. Jia and Y. Zhang, "Verifiable and Multi-Keyword Searchable Attribute-Based Encryption Scheme for Cloud Storage," in IEEE Access, vol. 7, pp. 50136-50147, 2019, doi: 10.1109/ACCESS.2019.2910828.

J. Sun, L. Ren, S. Wang and X. Yao, "Multi-Keyword Searchable and Data Verifiable Attribute-Based Encryption Scheme for Cloud Storage," in IEEE Access, vol. 7, pp. 66655-66667, 2019, doi: 10.1109/ACCESS.2019.2917772.

Mittal, P. ., & Navita. (2023). Early-Stage Detection of Covid-19 Patient using ML Model: A Case Study. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 84–89. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2480.

M. Morales-Sandoval, M. H. Cabello, H. M. Marin-Castro and J. L. G. Compean, "Attribute-Based Encryption Approach for Storage, Sharing and Retrieval of Encrypted Data in the Cloud," in IEEE Access, vol. 8, pp. 170101-170116, 2020, doi: 10.1109/ACCESS.2020.3023893.

Anthony Thompson, Ian Martin, Alejandro Perez, Luis Rodriguez, Diego Rodríguez. Utilizing Machine Learning for Educational Game Design. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/183.

H. Li, Y. Yang, Y. Dai, S. Yu and Y. Xiang, "Achieving Secure and Efficient Dynamic Searchable Symmetric Encryption over Medical Cloud Data," in IEEE Transactions on Cloud Computing, vol. 8, no. 2, pp. 484-494, 1 April-June 2020, doi: 10.1109/TCC.2017.2769645.

Mamta, B. B. Gupta, K. -C. Li, V. C. M. Leung, K. E. Psannis and S. Yamaguchi, "Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System," in IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 12, pp. 1877-1890, December 2021, doi: 10.1109/JAS.2021.1004003.

Y. Miao, R. H. Deng, X. Liu, K. -K. R. Choo, H. Wu and H. Li, "Multi-Authority Attribute-Based Keyword Search over Encrypted Cloud Data," in IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 4, pp. 1667-1680, 1 July-Aug. 2021, doi: 10.1109/TDSC.2019.2935044.

S. Niu, L. Chen, J. Wang and F. Yu, "Electronic Health Record Sharing Scheme With Searchable Attribute-Based Encryption on Blockchain," in IEEE Access, vol. 8, pp. 7195-7204, 2020, doi: 10.1109/ACCESS.2019.2959044.

P. Chaudhari and M. L. Das, "Privacy Preserving Searchable Encryption with Fine-Grained Access Control," in IEEE Transactions on Cloud Computing, vol. 9, no. 2, pp. 753-762, 1 April-June 2021, doi: 10.1109/TCC.2019.2892116.

H. Wang, X. Dong and Z. Cao, "Multi-Value-Independent Ciphertext-Policy Attribute Based Encryption with Fast Keyword Search," in IEEE Transactions on Services Computing, vol. 13, no. 6, pp. 1142-1151, 1 Nov.-Dec. 2020, doi: 10.1109/TSC.2017.2753231.

H. Yin et al., "CP-ABSE: A Ciphertext-Policy Attribute-Based Searchable Encryption Scheme," in IEEE Access, vol. 7, pp. 5682-5694, 2019, doi: 10.1109/ACCESS.2018.2889754.

Y. Miao, J. Ma, X. Liu, X. Li, Q. Jiang and J. Zhang, "Attribute-Based Keyword Search over Hierarchical Data in Cloud Computing," in IEEE Transactions on Services Computing, vol. 13, no. 6, pp. 985-998, 1 Nov.-Dec. 2020, doi: 10.1109/TSC.2017.2757467.

Pande, S. D., Kanna, R. K., & Qureshi, I. (2022). Natural Language Processing Based on Name Entity With N-Gram Classifier Machine Learning Process Through GE-Based Hidden Markov Model. Machine Learning Applications in Engineering Education and Management, 2(1), 30–39. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/22.

P. Chaudhari and M. L. Das, "KeySea: Keyword-Based Search With Receiver Anonymity in Attribute-Based Searchable Encryption," in IEEE Transactions on Services Computing, vol. 15, no. 2, pp. 1036-1044, 1 March-April 2022, doi: 10.1109/TSC.2020.2973570.

J. Gao and F. Zhou, "An Encrypted Cloud Email Searching and Filtering Scheme Based on Hidden Policy Ciphertext-Policy Attribute-Based Encryption With Keyword Search," in IEEE Access, vol. 10, pp. 8184-8193, 2022, doi: 10.1109/ACCESS.2021.3136331.

H. Yin, Y. Xiong, T. Deng, H. Deng and P. Zhu, "A Privacy-Preserving and Identity-Based Personalized Recommendation Scheme for Encrypted Tasks in Crowdsourcing," in IEEE Access, vol. 7, pp. 138857-138871, 2019, doi: 10.1109/ACCESS.2019.2943114.

L. Liu, S. Wang, B. He and D. Zhang, "A Keyword-Searchable ABE Scheme From Lattice in Cloud Storage Environment," in IEEE Access, vol. 7, pp. 109038-109053, 2019, doi: 10.1109/ACCESS.2019.2928455.

Y. Yu, J. Shi, H. Li, Y. Li, X. Du and M. Guizani, "Key-Policy Attribute-Based Encryption With Keyword Search in Virtualized Environments," in IEEE Journal on Selected Areas in Communications, vol. 38, no. 6, pp. 1242-1251, June 2020, doi: 10.1109/JSAC.2020.2986620.

M. Zeng, H. Qian, J. Chen and K. Zhang, "Forward Secure Public Key Encryption with Keyword Search for Outsourced Cloud Storage," in IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 426-438, 1 Jan.-March 2022, doi: 10.1109/TCC.2019.2944367.

Chaudhary, D. S. . (2021). ECG Signal Analysis for Myocardial Disease Prediction by Classification with Feature Extraction Machine Learning Architectures. Research Journal of Computer Systems and Engineering, 2(1), 06:10. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/12.

J. Niu, X. Li, J. Gao and Y. Han, "Blockchain-Based Anti-Key-Leakage Key Aggregation Searchable Encryption for IoT," in IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1502-1518, Feb. 2020, doi: 10.1109/JIOT.2019.2956322.

S. Khan et al., "ABKS-PBM: Attribute-Based Keyword Search With Partial Bilinear Map," in IEEE Access, vol. 9, pp. 46313-46324, 2021, doi: 10.1109/ACCESS.2021.3068194.

Natalia Volkova, Machine Learning Approaches for Stock Market Prediction , Machine Learning Applications Conference Proceedings, Vol 2 2022.

K. Gu, W. Zhang, X. Li and W. Jia, "Self-Verifiable Attribute-Based Keyword Search Scheme for Distributed Data Storage in Fog Computing With Fast Decryption," in IEEE Transactions on Network and Service Management, vol. 19, no. 1, pp. 271-288, March 2022, doi: 10.1109/TNSM.2021.3123475.