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International Journal of Advances in Engineering & Scientific Research

BLOCKCHAIN-BASED SECURE AND VERIFIABLE REMOTE SENSING IMAGE RETRIEVAL IN A CLOUD COMPUTING ENVIRONMENT

Mr. K. Anguraju, Praveena R, Shruti R, Thirisha S

Mr. K. Anguraju, Praveena R, Shruti R, Thirisha S, Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Tamilnadu, India

DOI : https://doi.org/10.5281/zenodo.14925027 Page No : 117-128

Published Online : 2024-06-30

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With the rapid expansion of remote sensing technology and cloud computing, vast amounts of remote sensing images are stored and processed in cloud environments. However, ensuring secure, verifiable, and efficient image retrieval remains a significant challenge due to data integrity concerns, unauthorized access, and computational overhead. This paper proposes a blockchain-based secure and verifiable remote sensing image retrieval framework that leverages smart contracts, cryptographic hashing, and distributed ledger technology to enhance security, privacy, and reliability in cloud-based image retrieval. The proposed system integrates a blockchain network to maintain an immutable audit trail, ensuring data integrity and verifiability of retrieval operations. To enhance privacy and efficiency, homomorphic encryption and Merkle tree-based indexing are employed, allowing secure searching without exposing sensitive image data. A hybrid indexing mechanism using content-based and metadata-based retrieval improves accuracy and search performance. Smart contracts enable automated access control and verification, reducing reliance on centralized authorities. Experimental evaluations demonstrate that the proposed framework significantly enhances security, retrieval accuracy, and computational efficiency compared to traditional cloud-based retrieval approaches. The blockchain integration ensures tamper-proof logging of access requests, while the cryptographic techniques safeguard against unauthorized data modifications. The proposed method achieves high retrieval precision, minimal latency, and robust resistance to adversarial attacks, making it a suitable solution for secure remote sensing image retrieval in cloud environments.

Keywords: Block chain, Remote Sensing, Cloud Computing, Secure Image Retrieval, Smart Contracts, Homomorphic Encryption, Merkle Tree, Data Integrity, Access Control, Tamper-Proof Logging..

References

Sun, J., Zhang, Y., Wu, Z., Zhu, Y., Yin, X., Ding, Z., ... & Plaza, A. (2019). An efficient and scalable framework for processing remotely sensed big data in cloud computing environments. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4294-4308.
Yogapriya, J., Saravanabhavan, C., Asokan, R., Vennila, I., Preethi, P., & Nithya, B. (2018). A study of image retrieval system based on feature extraction, selection, classification and similarity measurements. Journal of Medical Imaging and Health Informatics, 8(3), 479-484.
Suresh, K., Reddy, P. P., & Preethi, P. (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520.
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Li, Y., Zhang, Y., Huang, X., & Ma, J. (2018). Learning source-invariant deep hashing convolutional neural networks for cross-source remote sensing image retrieval. IEEE Transactions on Geoscience and Remote Sensing, 56(11), 6521-6536.
Tong, X. Y., Guo, C., & Cheng, H. (2020). Multi-source remote sensing image big data classification system design in cloud computing environment. International Journal of Internet Manufacturing and Services, 7(1-2), 130-145.
Ha, I., Kim, H., Park, S., & Kim, H. (2018). Image retrieval using BIM and features from pretrained VGG network for indoor localization. Building and Environment, 140, 23-31.
Huang, Q., Cervone, G., & Zhang, G. (2017). A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data. Computers, Environment and Urban Systems, 66, 23-37.
Guo, R., Zhuang, C., Shi, H., Zhang, Y., & Zheng, D. (2020). A lightweight verifiable outsourced decryption of attribute-based encryption scheme for blockchain-enabled wireless body area network in fog computing. International journal of distributed sensor networks, 16(2), 1550147720906796.

Anguraju K, Praveena R, Shruti R, & Thirisha S (2024), “Blockchain-Based Secure And Verifiable Remote Sensing Image Retrieval In A Cloud Computing Environment”, International Journal of Advances in Engineering & Scientific Research, Volume 11, Issue 1, 2024, pp 117-128. DOI : https://doi.org/10.5281/zenodo.14925027

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References

Sun, J., Zhang, Y., Wu, Z., Zhu, Y., Yin, X., Ding, Z., ... & Plaza, A. (2019). An efficient and scalable framework for processing remotely sensed big data in cloud computing environments. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4294-4308.
Yogapriya, J., Saravanabhavan, C., Asokan, R., Vennila, I., Preethi, P., & Nithya, B. (2018). A study of image retrieval system based on feature extraction, selection, classification and similarity measurements. Journal of Medical Imaging and Health Informatics, 8(3), 479-484.
Suresh, K., Reddy, P. P., & Preethi, P. (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520.
Siddique R, Nivedhitha MS, Jacob B. Quantitative analysis for detection of toxic elements in various irrigants, their combination (precipitate), and para-chloroaniline: An inductively coupled plasma mass spectrometry study. J Conserv Dent. 2019 Jul-Aug;22(4):344-350. doi: 10.4103/JCD.JCD_95_19.
Qin, J., Li, H., Xiang, X., Tan, Y., Pan, W., Ma, W., & Xiong, N. N. (2019). An encrypted image retrieval method based on Harris corner optimization and LSH in cloud computing. IEEE Access, 7, 24626-24633.
Li, Y., Zhang, Y., Huang, X., & Ma, J. (2018). Learning source-invariant deep hashing convolutional neural networks for cross-source remote sensing image retrieval. IEEE Transactions on Geoscience and Remote Sensing, 56(11), 6521-6536.
Tong, X. Y., Guo, C., & Cheng, H. (2020). Multi-source remote sensing image big data classification system design in cloud computing environment. International Journal of Internet Manufacturing and Services, 7(1-2), 130-145.
Ha, I., Kim, H., Park, S., & Kim, H. (2018). Image retrieval using BIM and features from pretrained VGG network for indoor localization. Building and Environment, 140, 23-31.
Huang, Q., Cervone, G., & Zhang, G. (2017). A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data. Computers, Environment and Urban Systems, 66, 23-37.
Guo, R., Zhuang, C., Shi, H., Zhang, Y., & Zheng, D. (2020). A lightweight verifiable outsourced decryption of attribute-based encryption scheme for blockchain-enabled wireless body area network in fog computing. International journal of distributed sensor networks, 16(2), 1550147720906796.

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