Neural Network Based Data Encryption: A Comparison Study among DES, AES, and HE Techniques

Sin-Qian Yeow - Multimedia University, 63100 Cyberjaya, Selangor, Malaysia
Kok-Why Ng - Multimedia University, 63100 Cyberjaya, Selangor, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.7.3-2.2336

Abstract


With the improvement of technology and the continuous expansion and deepening of neural network technology, its application in computer network security plays an important role. However, the development of neural networks is accompanied by new threats and challenges. This paper proposes to encrypt the weight data using encryption algorithms and embed image encryption algorithms to improve protected data security further. The purpose is to address the feasibility and effectiveness of using modern encryption algorithms for data encryption in machine learning in response to data privacy breaches. The approach consists of training a neural network to simulate a model of machine learning and then encrypting it using Data Encryption Standard (DES), Advanced Encryption Standard (AES), and Homomorphic Encryption (HE) techniques, respectively. Its performance is evaluated based on the encryption/decryption accuracy and computational efficiency. The results indicate that combining DES with Blowfish offers moderate encryption and decryption speeds but is less secure than AES and HE. AES provides a practical solution, balancing security and performance, offering a relatively swift encryption and decryption process while maintaining high security. However, Fernet and HE present a viable alternative if data privacy is a top priority. Encryption and decryption times increase with file size and require sufficient computational resources. Future research should explore image encryption techniques to balance security and accurate image retrieval during decryption. Advanced privacy-preserving approaches, such as differential privacy and secure multi-party computation, may enhance security and confidentiality in digital encryption and decryption processes.

Keywords


Cryptography; data encryption standard; advanced encryption standard; homomorphic encryption; image encryption; machine learning

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References


T. Munusamy and T. Khodadi, “Building Cyber Resilience: Key Factors for Enhancing Organizational Cyber Security,†Journal of Informatics and Web Engineering, vol. 2, no. 2, pp. 59–71, Sep. 2023, doi: 10.33093/jiwe.2023.2.2.5.

P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A Review of Yolo Algorithm Developments,†in Procedia Computer Science, Elsevier B.V., 2021, pp. 1066–1073. doi: 10.1016/j.procs.2022.01.135.

J. Terven and D. M. Cordova-Esparza, “A Comprehensive Review of YOLO: From YOLOv1 and Beyond,†arXiv preprint, Apr. 2023, [Online]. Available: http://arxiv.org/abs/2304.00501

A. Dalvi, A. Jain, S. Moradiya, R. Nirmal, J. Sanghavi, and I. Siddavatam, “Securing Neural Networks Using Homomorphic Encryption,†in International Conference on Intelligent Technologies, CONIT 2021, Jun. 2021, pp. 1–7. doi: 10.1109/CONIT51480.2021.9498376.

K. Nandakumar, N. Ratha, S. Pankanti, and S. Halevi, “Towards deep neural network training on encrypted data,†in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE Computer Society, Jun. 2019, pp. 40–48. doi: 10.1109/CVPRW.2019.00011.

J. W. Lee et al., “Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network,†IEEE Access, vol. 10, pp. 30039–30054, 2022, doi: 10.1109/ACCESS.2022.3159694.

G. Onoufriou, M. Hanheide, and G. Leontidis, “EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting,†arXiv preprint, Oct. 2022, [Online]. Available: http://arxiv.org/abs/2110.13638

S. Hong, J. H. Park, W. Cho, H. Choe, and J. H. Cheon, “Secure tumor classification by shallow neural network using homomorphic encryption,†BMC Genomics, vol. 23, no. 1, Dec. 2022, doi: 10.1186/s12864-022-08469-w.

D. Nugent, “Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption,†arXiv preprint, Nov. 2022, [Online]. Available: http://arxiv.org/abs/2211.06675

J.-W. Lee et al., “Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network,†IEEE Access, vol. 10, pp. 30039–30054, Jun. 2022, doi: 10.1109/ACCESS.2022.3159694.

Q. Lou and L. Jiang, “SHE: A Fast and Accurate Deep Neural Network for Encrypted Data,†Adv Neural Inf Process Syst, May 2019, [Online]. Available: http://arxiv.org/abs/1906.00148

Y. K. Yasin, P. Siddeeq, Y. Ameen, D. Hassan, and A. Chiad, “Advanced Encryption Standard (AES) Enhancement Using Artificial Neural Networks,†Int J Sci Eng Res, vol. 5, no. 10, 2014, [Online]. Available: http://www.ijser.org

Siddeeq. Y. Ameen and A. H. Mahdi, “AES Cryptosystem Development Using Neural Networks,†International Journal of Computer and Electrical Engineering, vol. 3(2), pp. 315–318, 2011, doi: 10.7763/IJCEE.2011.V3.333.

V. Lytvyn, I. Peleshchak, R. Peleshchak, and V. Vysotska, “Information Encryption Based on the Synthesis of a Neural Network and AES Algorithm,†2019 3rd International Conference on Advanced Information and Communications Technologies, AICT 2019 - Proceedings, pp. 447–450, Jul. 2019, doi: 10.1109/AIACT.2019.8847896.

H. Kwon, H. Yoon, and K. W. Park, “Multi-Targeted Backdoor: Indentifying Backdoor Attack for Multiple Deep Neural Networks,†IEICE Trans Inf Syst, vol. E103.D, no. 4, pp. 883–887, Apr. 2020, doi: 10.1587/TRANSINF.2019EDL8170.

Sangeetha S and Haseena P, “Image Encryption using Deep Neural Networks based Chaotic Algorithm,†International Research Journal of Engineering and Technology, 2020, Accessed: Oct. 21, 2023. [Online]. Available: www.irjet.net

Y. A. Liu et al., “A dynamic AES cryptosystem based on memristive neural network,†Scientific Reports 2022 12:1, vol. 12, no. 1, pp. 1–11, Jul. 2022, doi: 10.1038/s41598-022-13286-y.

R. Peleshchak, V. Lytvyn, N. Kholodna, I. Peleshchak, and V. Vysotska, “Two-Stage AES Encryption Method Based on Stochastic Error of a Neural Network,†Proceedings - 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2022, pp. 381–385, 2022, doi: 10.1109/TCSET55632.2022.9766991.

A. Mundra, S. Mundra, J. S. Srivastava, and P. Gupta, “Optimized deep neural network for cryptanalysis of DES,†Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5921–5931, May 2020, doi: 10.3233/JIFS-179679.

Y. Chauvin, “A Back-Propagation Algorithm with Optimal Use of Hidden Units,†Neural Information Processing Systems, 1988.

N. Dhia and K. Al-Shakarchy, “Simulating DES Algorithm Using Artificial Neural Network,†Journal of Kerbala University, vol. 10, 2012.

M. A. B. Farah, R. Guesmi, A. Kachouri, and M. Samet, “A new design of cryptosystem based on S-box and chaotic permutation,†Multimed Tools Appl, vol. 79, no. 27–28, pp. 19129–19150, Jul. 2020, doi: 10.1007/S11042-020-08718-8/METRICS.

Z. Tolba, M. Derdour, M. A. Ferrag, S. M. Muyeen, and M. Benbouzid, “Automated Deep Learning BLACK-BOX Attack for Multimedia P-BOX Security Assessment,†IEEE Access, vol. 10, pp. 94019–94039, 2022, doi: 10.1109/ACCESS.2022.3204175.

R. Záluský, D. ÄŽuraÄková, V. Sedlák, and T. KováÄik, “The Use of Neural Network For Data Encryption Standard (DES),†May 2013.

S. Fan and Y. Zhao, “Analysis of des Plaintext Recovery Based on BP Neural Network,†Security and Communication Networks, vol. 2019, 2019, doi: 10.1155/2019/9580862.

A. Benamira, D. Gerault, T. Peyrin, and Q. Q. Tan, “A Deeper Look at Machine Learning-Based Cryptanalysis,†Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12696 LNCS, pp. 805–835, 2021, doi: 10.1007/978-3-030-77870-5_28/COVER.

Y. E. Yousif, “IMPROVING THE EFFICIENCY OF DES ALGORITHM USING NEURAL NETWORKS,†International Journal of Engineering Applied Sciences and Technology, vol. 5, no. 1, pp. 26–29, May 2020, doi: 10.33564/IJEAST.2020.V05I01.004.

K. M. Alallayah, W. F. Abd El-Wahed, M. Amin, and A. H. Alhamami, “Attack of Against Simplified Data Encryption Standard Cipher System Using Neural Networks,†Journal of Computer Science, vol. 6, no. 1, pp. 29–35, Jan. 2010, doi: 10.3844/JCSSP.2010.29.35.

K. M. Alallayah, A. H. Alhamami, W. A. El-Wahed, and M. Amin, “Applying neural networks for simplified data encryption standard (SDES) cipher system cryptanalysis,†˜The œinternational Arab journal of information technology, 2012.

Q. Hu, L. Ma, and J. Zhao, “DeepGraph: A PyCharm Tool for Visualizing and Understanding Deep Learning Models,†Proceedings - Asia-Pacific Software Engineering Conference, APSEC, vol. 2018-December, pp. 628–632, Jul. 2018, doi: 10.1109/APSEC.2018.00079.

P. van Lunteren, “EcoAssist: A no-code platform to train and deploy custom YOLOv5 object detection models,†J Open Source Softw, vol. 8, no. 88, p. 5581, Aug. 2023, doi: 10.21105/JOSS.05581.

H. Dibas and K. E. Sabri, “A comprehensive performance empirical study of the symmetric algorithms:AES, 3DES, Blowfish and Twofish,†2021 International Conference on Information Technology, ICIT 2021 - Proceedings, pp. 344–349, Jul. 2021, doi: 10.1109/ICIT52682.2021.9491644.

E. Karuna Wijaya, R. Kumala, and B. Soewito, “IMPROVING SECURITY AND IMPERCEPTIBILITY USING MODIFIED LEAST SIGNIFICANT BIT AND FERNET SYMMETRIC ENCRYPTION,†J Theor Appl Inf Technol, vol. 15, p. 17, 2022, Accessed: Oct. 21, 2023. [Online]. Available: www.jatit.org

M. Ghadamyari and S. Samet, “Privacy-Preserving Statistical Analysis of Health Data Using Paillier Homomorphic Encryption and Permissioned Blockchain,†Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, pp. 5474–5479, Dec. 2019, doi: 10.1109/BIGDATA47090.2019.9006231.

A. A. Alqarni, “A Secure Approach for Data Integration in Cloud using Paillier Homomorphic Encryption,†Albaha University Journal of Basic and Applied Sciences, vol. 5, no. 2, pp. 15–21, 2021, Accessed: Oct. 21, 2023. [Online]. Available: https://portal.bu.edu.sa/web/jbas/

T. B. Ogunseyi and T. Bo, “Fast Decryption Algorithm for Paillier Homomorphic Cryptosystem,†Proceedings of 2020 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2020, pp. 803–806, Jul. 2020, doi: 10.1109/ICPICS50287.2020.9202325.

G. Grispos and K. Bastola, “Cyber autopsies: The integration of digital forensics into medical contexts,†Proc IEEE Symp Comput Based Med Syst, vol. 2020-July, pp. 510–513, Jul. 2020, doi: 10.1109/CBMS49503.2020.00102.

E. Leierzopf, V. Mikhalev, N. Kopal, B. Esslinger, H. Lampesberger, and E. Hermann, “Detection of Classical Cipher Types with Feature-Learning Approaches,†Communications in Computer and Information Science, vol. 1504 CCIS, pp. 152–164, 2021, doi: 10.1007/978-981-16-8531-6_11/COVER.

G. Wang and G. Wang, “Improved Differential-ML Distinguisher: Machine Learning Based Generic Extension for Differential Analysis,†Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12919 LNCS, pp. 21–38, 2021, doi: 10.1007/978-3-030-88052-1_2/TABLES/6.

M. Cao and W. Zhang, “Related-Key Differential Cryptanalysis of the Reduced-Round Block Cipher GIFT,†IEEE Access, vol. 7, pp. 175769–175778, 2019, doi: 10.1109/ACCESS.2019.2957581.

Adam Bertram, “How to Use WinMerge to Compare Files,†Ipswitch.

X. Zhang, L. Wang, G. Cui, and Y. Niu, “Entropy-Based Block Scrambling Image Encryption Using des Structure and Chaotic Systems,†Int J Opt, vol. 2019, 2019, doi: 10.1155/2019/3594534.

D. M. Alsaffar et al., “Image Encryption Based on AES and RSA Algorithms,†ICCAIS 2020 - 3rd International Conference on Computer Applications and Information Security, Mar. 2020, doi: 10.1109/ICCAIS48893.2020.9096809.

H. Singh, “Advanced Image Processing Using OpenCV,†Practical Machine Learning and Image Processing, pp. 63–88, 2019, doi: 10.1007/978-1-4842-4149-3_4.

W. P. Sari and H. Fahmi, “The Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning,†Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 6, no. 3, pp. 187–194, Aug. 2021, doi: 10.22219/KINETIK.V6I3.1272.

K. Sharma, A. Aggarwal, T. Singhania, D. Gupta, and A. Khanna, “Hiding Data in Images Using Cryptography and Deep Neural Network,†Journal of Artificial Intelligence and Systems, vol. 1, no. 1, pp. 143–162, Dec. 2019, doi: 10.33969/AIS.2019.11009.

J. Deepika, C. Rajan, and T. Senthil, “Security and Privacy of Cloud- And IoT-Based Medical Image Diagnosis Using Fuzzy Convolutional Neural Network,†Comput Intell Neurosci, vol. 2021, 2021, doi: 10.1155/2021/6615411.

X. Chai, Y. Wang, Z. Gan, X. Chen, and Y. Zhang, “Preserving privacy while revealing thumbnail for content-based encrypted image retrieval in the cloud,†Inf Sci (N Y), vol. 604, pp. 115–141, Aug. 2022, doi: 10.1016/J.INS.2022.05.008.

J. Jain and A. Jain, “Securing E-Healthcare Images Using an Efficient Image Encryption Model,†Sci Program, vol. 2022, 2022, doi: 10.1155/2022/6438331.

M. U. Hassan, M. H. Rehmani, and J. Chen, “Differential Privacy Techniques for Cyber Physical Systems: A Survey,†IEEE Communications Surveys and Tutorials, vol. 22, no. 1, pp. 746–789, Jan. 2020, doi: 10.1109/COMST.2019.2944748.

B. Knott, S. Venkataraman, A. Hannun, S. Sengupta, M. Ibrahim, and L. van der Maaten, “CrypTen: Secure Multi-Party Computation Meets Machine Learning,†Adv Neural Inf Process Syst, vol. 7, pp. 4961–4973, Sep. 2021, Accessed: Oct. 21, 2023. [Online]. Available: https://arxiv.org/abs/2109.00984v2