A MODEL OF CLUSTERING OF ENCRYPTED DATA DURING TRANSMISSION OVER A DECENTRALIZED NETWORK BASED ON THE AES CRYPTOGRAPHIC ALGORITHM

DOI: 10.31673/2409-7292.2025.014570

Abstract

The paper considers a model for clustering encrypted data during transmission in decentralized networks based on the
AES cryptographic algorithm. The growth in the volume of transmitted information in systems such as blockchain, distributed
computing, and IoT requires the development of new approaches to ensuring confidentiality and efficiency of data processing.
The main problem is that traditional encryption models significantly complicate the implementation of clustering due to the
need for preliminary decryption. The proposed model uses homomorphic encryption and a developed algorithm based on AES
with dynamic key update, which allows grouping data without violating their confidentiality. The clustering model is based on
the use of a homomorphic distance function, which allows determining the similarity between encrypted data blocks without
decrypting them. This allows improving the security of information processing, minimizing the risks of data leakage.
Additionally, a mechanism for dynamically updating encryption keys after each round is proposed, which significantly
complicates cryptanalytic attacks. A model of adding controlled noise to the ciphertext is also implemented, which reduces the
probability of analysis of encrypted data and increases the security of transmitted messages. The study demonstrates that the
proposed clustering model has advantages over traditional approaches that involve decryption before grouping data.
Performance analysis confirms that the use of homomorphic analysis allows to reduce computational costs and maintain high
processing speed. The results obtained indicate the effectiveness of the developed model for application in decentralized
networks, especially in systems working with large amounts of confidential information, such as financial technologies,
blockchain and the Internet of Things.
Keywords: clustering, encrypted data, decentralized network, homomorphic encryption, AES algorithm, confidentiality,
security.

References
1. Hurtado Ramírez D. & Auñón J.M. (2020). Privacy Preserving K-Means Clustering: A Secure Multi-Party
Computation Approach. https://arxiv.org/pdf/2009.10453
2. Li Q. & Luo L. (2023). On the Privacy of Federated Clustering: A Cryptographic View.
https://arxiv.org/pdf/2312.07992
3. Aggarwal C.C. & Reddy C.K. (2013). Data Clustering: Algorithms and Applications. CRC Press.
4. Berkhin P. (2006). A Survey of Clustering Data Mining Techniques. In Grouping Multidimensional Data
(pp. 25–71). Springer.
5. Jain A., Murty M. & Flynn P. (1999). Data Clustering: A Review. ACM Computing Surveys.
6. Schubert E., Sander J., Ester M., Kriegel H.P. & Xu X. (2022). DBSCAN: Why and How You Should (Still)
Use DBSCAN. ACM Transactions on Database Systems.
7. Xu R. & Wunsch D. (2005). Survey of Clustering Algorithms. IEEE Transactions on Neural Networks, 16(3),
645–678.
8. Ng R.T. & Han J. (2002). CLARANS: A Method for Clustering Objects for Spatial Data Mining. IEEE
Transactions on Knowledge and Data Engineering, 14(5), 1003–1016.
9. Сабов Д.П. & Шаркаді М.М. (2023). Підходи щодо кластеризації криптовалют. Науковий вісник
Ужгородського університету. Серія «Математика і інформатика», 42(1), 201–207.
https://doi.org/10.24144/2616-7700.2023.42(1).201-207
10. Yang Y., Cer D. & Ahmad A. (2019). Multilingual Universal Sentence Encoder for Semantic Retrieval.
11. Аріткулова Ю.Р. (2024). Кластеризація sybil-адрес на блокчейні методами машинного навчання.
https://openarchive.nure.ua/handle/document/27730
12. Hastie Trevor, Tibshirani Robert, Friedman Jerome (2019) «The EM algorithm» – New York: Springer. –
С. 236–242.
13. Zobaed S.M. & Salehi M.A. (2020). Privacy-Preserving Clustering of Unstructured Big Data for CloudBased Enterprise Search Solutions. https://arxiv.org/pdf/2005.11317
14. Kaufman L. & Rousseeuw P.J. (2009). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.
15. Yinfei Yang, Daniel Cer, Amin Ahmad (2019). «Multilingual Universal Sentence Encoder for Semantic
Retrieval».
16. Ester M., Kriegel H.P., Sander J. & Xu X. (1996). A Density-Based Algorithm for Discovering Clusters in
Large Spatial Databases with Noise. In Proceedings of the Second International Conference on Knowledge Discovery
and Data Mining (pp. 226–231).
17. Пількевич І.А., Бойченко О.С. & Гуменюк І.В. (2019). Метод децентралізованого управління
мережевими ресурсами інформаційно-комунікаційних мереж. Технічна інженерія, 2(84), 100–108.
https://journals.uran.ua/index.php/2706-5847/article/view/186576
18. Estivill-Castro V. (2002). Why So Many Clustering Algorithms: A Position Paper. ACM SIGKDD
Explorations Newsletter, 4(1), 65–75.
19. Guha S., Rastogi R. & Shim K. (1998). CURE: An Efficient Clustering Algorithm for Large Databases. In
Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data (pp. 73–84).
20. Mirkes E.M. (2021). «K-means and K-medoids applet».