Adaptive Serial Cascaded Deep Network-based Data Deduplication Mechanism with Hyper-Elliptic Curve Cryptography for Encryption in Cloud Environment


  • Manjunath Singh H., Tanuja R.


Data Deduplication; Cloud Environment; Optimized Serial Cascaded Deep Network; Enhanced Red-Tailed Hawk algorithm; Hyper-Elliptic Curve Cryptography with Optimal Key.


Cloud storage services utilize deduplication to optimize capacity and minimize bandwidth demands. This process efficiently reduces redundant data to a single instance, thereby conserving storage space. Deduplication is particularly effective when multiple users upload identical information to the cloud. However, deduplication poses challenges related to security and copyright issues. Implementing secure deduplication can significantly cut down on both storage and communication costs in cloud services, making it highly relevant in the era of big data. Systems that verify the proof-of-ownership allow individuals who have uploaded the same data to credibly assert their ownership to the cloud service. However, the common practice of encrypting data before uploading it for privacy reasons complicates deduplication efforts because encryption introduces randomness that prevents identifying duplicates. To overcome this, various schemes have been introduced that permit users to encrypt data with a common key for identical data sets. Nevertheless, many of these schemes are susceptible to security flaws, particularly not addressing the frequent changes in data ownership in a dynamic cloud storage environment. Therefore, creating a secure data deduplication model that overcomes the limitations of current approaches is essential. The implemented framework consists of data collection, deduplication phase and encryption. Initially, attributes likes “filename, size, block name, size, file-type hash tag, file location, file updated date and data pattern” are used for the deduplication process. Next, the collected data is provided as the input to the Optimized Serial Cascaded Deep Network (OSCDN)-based data deduplication model, which is the fusion of “Deep Belief Network (DBN) with Dilated Convolution Long Short Term Memory (DConv-LSTM)”, Here the parameters of OSCDN is tuned using “Enhanced Red-Tailed Hawk algorithm (ERTH)”. Further, the de-duplicated data is encrypted using “Hyper-Elliptic Curve Cryptography with Optimal Key (HECC-OK)”. In this setup, the ERTH algorithm selects keys in the most optimal manner. Subsequently, the encrypted data is stored on the cloud platform. The developed architecture then undergoes several experimental validations to showcase its enhanced performance rate relative to traditional deduplication methods.


Download data is not yet available.


Luo, Shengmei, Guangyan Zhang, Chengwen Wu, Samee U. Khan, and Keqin Li, "Boafft: Distributed deduplication for big data storage in the cloud," IEEE transactions on cloud computing, vol. 8, no. 4, pp.1199-1211, 2015.

Yan, Zheng, Mingjun Wang, Yuxiang Li, and Athanasios V. Vasilakos, "Encrypted data management with deduplication in cloud computing," IEEE Cloud Computing, vol. 3, no. 2, pp. 28-35, 2016.

B. Mao, H. Jiang, S. Wu and L. Tian, "Leveraging Data Deduplication to Improve the Performance of Primary Storage Systems in the Cloud," IEEE Transactions on Computers, vol. 65, no. 6, pp. 1775-1788, 1 June 2016.

Sharma, Shivi, and Hemraj Saini, "Fog assisted task allocation and secure deduplication using 2FBO2 and MoWo in cluster-based industrial IoT (IIoT)," Computer Communications, vol. 152, pp. 187-199, 2020.

Muthunagai, S. U, and R. Anitha, "CTS-IIoT: Computation of Time Series Data During Index Based De-duplication of Industrial IoT (IIoT) Data in Cloud Environment," Wireless Personal Communications, vol. 129, no. 1, pp. 433-453, 2023.

Oladayo Olufemi Olakanmi, Kehinde Oluwasesan Odeyemi, "Faster and efficient cloud-server-aided data de-duplication scheme with an authenticated key agreement for Industrial Internet-of-Things," Internet of Things, Vol. 14, No. 100376, June 2021.

Muthunagai, S. U, and R. Anitha, "TDOPS: Time series based deduplication and optimal data placement strategy for IIoT in cloud environment," Journal of Intelligent & Fuzzy Systems, vol.43, no. 1, pp. 1583-1597, 2022.

Vignesh, R, and J. Preethi, "Secure Data Deduplication System with Efficient and Reliable Multi-Key Management in Cloud Storage," Journal of Internet Technology, vol. 23, no. 4, pp. 811-825, 2022.

Yang, Xue, Rongxing Lu, Jun Shao, Xiaohu Tang, and Ali A. Ghorbani, "Achieving efficient and privacy-preserving multi-domain big data deduplication in cloud," IEEE Transactions on Services Computing, vol. 14, no. 5, pp.1292-1305, 2018.

Gao, Yuan, Liquan Chen, Jinguang Han, Ge Wu, and Suhui Liu, "Similarity-based deduplication and secure auditing in IoT decentralized storage," Journal of Systems Architecture, vol. 142, pp. 102961, 2023.

Prathima, Ch, Naresh Babu Muppalaneni, and K. G. Kharade, "Deduplication of IoT data in cloud storage," Machine Learning and Internet of Things for Societal Issues, pp. 147-157. Springer Nature Singapore, 2022.

Yoosuf, Mohamed Sirajudeen, and R. Anitha, "Low latency fog-centric deduplication approach to reduce IoT healthcare data redundancy," Wireless Personal Communications, vol. 126, no. 1, pp. 421-443, 2022.

A. Vijayakumar and Dr. A. Nisha Jebaseeli, "Pioneer approach data deduplication to remove redundant data from cloud storage," International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 11, No. 10, pp. 535-544, October 2020.

Patra, Sudhansu Shekhar, Sudarson Jena, Jnyana Ranjan Mohanty, and Mahendra Kumar Gourisaria. "DedupCloud: an optimized efficient virtual machine deduplication algorithm in cloud computing environment." In Data deduplication approaches, pp. 281-306. Academic Press, 2021.

Kumar, Anil, and C. P. Shantala. "An extensive research survey on data integrity and deduplication towards privacy in cloud storage." International Journal of Electrical and Computer Engineering 10, no. 2, 2020.

Periasamy, J. K., and B. Latha. "Efficient hash function based duplication detection algorithm for data Deduplication deduction and reduction." Concurrency and Computation: Practice and Experience, vol. 33, no. 3, 2021.

Vignesh, R., and J. Preethi. "Secure Data Deduplication System with Efficient and Reliable Multi-Key Management in Cloud Storage." Journal of Internet Technology, vol. 23, no. 4, Pp. 811-825, 2022.

Gao, Yuan, Hequn Xian, and Aimin Yu, "Secure data deduplication for Internet-of-things sensor networks based on threshold dynamic adjustment," International Journal of Distributed Sensor Networks, vol. 16, no. 3, pp. 1550147720911003, 2020.

Fu, Yinjin, Nong Xiao, Hong Jiang, Guyu Hu, and Weiwei Chen, "Application-aware big data deduplication in cloud environment," IEEE transactions on cloud computing, vol. 7, no. 4, pp. 921-934, 2017.

Batham, Surabhi, Ritu Prasad, Praneet Saurabh, and Bhupendra Verma, "A new approach for data security using deduplication over cloud data storage," Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), pp. 26-27, 2018.

PG, Shynu, Nadesh RK, Varun G. Menon, Venu P, Mahdi Abbasi, and Mohammad R. Khosravi, "A secure data deduplication system for integrated cloud-edge networks," Journal of Cloud Computing, vol. 9, no. 1,pp. 61, 2020.

Hurst, Aaron, Daniel E. Lucani, and Qi Zhang, "GreedyGD: Enhanced generalized deduplication for direct analytics in IoT," IEEE Transactions on Industrial Informatics, 2024.

Wu, Zeng, Hui Huang, Yuping Zhou, and Chenhuang Wu, "A secure and efficient data deduplication framework for the internet of things via edge computing and blockchain," Connection Science, vol 34, no. 1, pp. 1999-2025, 2022.

Muthunagai, S. U., and R. Anitha, "Computing Time Series Data During Index Based De-Duplication of Industrial IoT Data in Cloud Environment," 2021.

Yang, Ye, Xiaofang Li, Dongjie Zhu, Hao Hu, Haiwen Du, Yundong Sun, Weiguo Tian, Yansong Wang, Ning Cao, and Gregory MP O’Hare, "A resource-constrained edge IoT device data-deduplication method with dynamic asymmetric maximum," Intelligent Automation & Soft Computing, vol.30, no. 2, pp. 481-494, 2021.

Seydali Ferahtia, Azeddine Houari, Hegazy Rezk, Ali Djerioui, Mohamed Machmoum, Saad Motahhir & Mourad Ait-Ahmed, "Red-tailed hawk algorithm for numerical optimization and real-world problems, "Scientific Reports, vol. 13, no. 12950, 2023.

1.Abdolkarim Mohammadi-Balani, Mahmoud Dehghan Nayeri, Adel Azar , Mohammadreza Taghizadeh-Yazdi," Golden eagle optimizer: A nature-inspired metaheuristic algorithm",Computers & Industrial Engineering,Volume 152, February 2021.

Dawid Połap and Marcin Woźniak," Red fox optimization algorithm",Expert Systems with Application, Volume 166, 15 March 2021.

Funda Kutlu Onay," A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems",Mathematics and Computers in Simulation,Vol. 212, October 2023.

M. S. Mehmood, M. R. Shahid, A. Jamil, R. Ashraf, T. Mahmood and A. Mehmood, "A Comprehensive Literature Review of Data Encryption Techniques in Cloud Computing and IoT Environment," 2019 8th International Conference on Information and Communication Technologies (ICICT), Karachi, Pakistan, 2019.

Xinmiao Zhang and K. K. Parhi, "Implementation approaches for the Advanced Encryption Standard algorithm," in IEEE Circuits and Systems Magazine, vol. 2, no. 4, pp. 24-46, 2002.

P. Wanda, Selo and B. S. Hantono, "Efficient message security based Hyper Elliptic Curve Cryptosystem (HECC) for Mobile Instant Messenger," 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering, Semarang, Indonesia, 2014.

G. E. Dahl, D. Yu, L. Deng and A. Acero, "Large vocabulary continuous speech recognition with context-dependent DBN-HMMS," 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 2011.

T. N. Sainath, O. Vinyals, A. Senior and H. Sak, "Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks," 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, Australia, 2015.




How to Cite

Manjunath Singh H. (2024). Adaptive Serial Cascaded Deep Network-based Data Deduplication Mechanism with Hyper-Elliptic Curve Cryptography for Encryption in Cloud Environment. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3718 –. Retrieved from



Research Article