Design of Privacy Preservation Model for Data Stream Using Condensation Based Anonymization
N/A
DOI:
https://doi.org/10.19153/cleiej.28.4.8Keywords:
Money Laundering, Privacy preservation, Data stream,K-Anonymity, Condensation MethodAbstract
In modern world, continuous data flows that may be analyzed in real-time and are created from multiple sources are referred to as data streams. As data streams become increasingly prevalent across various sectors, preserving the privacy of sensitive information while maintaining data utility is a significant challenge. Due to privacy concerns in most of the organizations, data are shared with third party or the public. In many cases, users are hesitant to provide personal information. This paper proposes a privacy-preservation model for data streams based on condensation-based anonymization. This model uses a condensation mechanism to reduce the granularity of data while ensuring that personally identifiable information and sensitive attributes are effectively anonymized. By leveraging a condensation approach, the model selectively compresses data, allowing for both privacy protection and the retention of essential data patterns for analysis. The condensation method minimizes lost information throughout the anonymization process in an effort to maintain the statistical features of the data. Using generalization and suppression approaches, k-Anonymity obscures identifying information in datasets, making it a useful tool for protecting individual privacy. The proposed model employs hybrid of K- Anonymity algorithm and condensation method to enhance the security and privacy of the data.
References
H. Gandhi, K. Tandon, S. Gite, B. Pradhan, A. J. I. J. o. S. S. Alamri, and I. Systems, "Navigating the Complexity of Money Laundering: Anti–money Laundering Advancements with AI/ML Insights," vol. 17, no. 1, 2024.
M. Tiwari, J. Ferrill, and D. M. J. J. o. A. L. Allan, "Trade-based money laundering: a systematic literature review," 2024.
T. Pousette and A. Rosendal, "Explainable Anti-Money Laundering," 2024.
F. J. E. E. A. i. A. D. A. Wójcik, "An Analysis of Novel Money Laundering Data Using Heterogeneous Graph Isomorphism Networks. FinCEN Files Case Study," vol. 28, no. 2, pp. 32-49, 2024.
M. Nazzari, "THE BEHAVIORAL HETEROGENEITY OF MONEY LAUNDERING," 2024.
C.-H. Tai and T.-J. Kan, "Identifying money laundering accounts," in 2019 International Conference on System Science and Engineering (ICSSE), 2019, pp. 379-382: IEEE.
A. J. I. M. l. j. Kotagiri and C. Engineering, "AML Detection and Reporting with Intelligent Automation and Machine learning," vol. 7, no. 7, pp. 1-17, 2024.
I. Ullah and Q. H. J. I. A. Mahmoud, "Design and development of a deep learning-based model for anomaly detection in IoT networks," vol. 9, pp. 103906-103926, 2021.
R. J. A. J. o. A. I. Avacharmal and S. Development, "Leveraging Supervised Machine Learning Algorithms for Enhanced Anomaly Detection in Anti-Money Laundering (AML) Transaction Monitoring Systems: A Comparative Analysis of Performance and Explainability," vol. 1, no. 2, pp. 68-85, 2021.
D. V. Kute, B. Pradhan, N. Shukla, A. J. I. J. o. S. S. Alamri, and I. Systems, "Explainable deep learning model for predicting money laundering transactions," vol. 17, no. 1, 2024.
M. E. J. J. o. A. S. R. Lokanan, "Predicting money laundering using machine learning and artificial neural networks algorithms in banks," vol. 19, no. 1, pp. 20-44, 2024.
J. F. M. Pazos, J. G. González, D. B. Lorenzo, and J. A. R. J. J. o. E. C. T. Morales, "Fraud Transaction Detection For Anti-Money Laundering Systems Based On Deep Learning," vol. 3, no. 1, pp. 29-34, 2024.
D. Vassallo, V. Vella, and J. J. S. C. S. Ellul, "Application of gradient boosting algorithms for anti-money laundering in cryptocurrencies," vol. 2, no. 3, p. 143, 2021.
D. Labanca, L. Primerano, M. Markland-Montgomery, M. Polino, M. Carminati, and S. J. I. A. Zanero, "Amaretto: An active learning framework for money laundering detection," vol. 10, pp. 41720-41739, 2022.
R. I. T. Jensen and A. J. I. A. Iosifidis, "Fighting money laundering with statistics and machine learning," vol. 11, pp. 8889-8903, 2023.
A. Kumar, S. Das, and V. Tyagi, "Anti Money Laundering detection using Na?ve Bayes Classifier."
M. Weber et al., "Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics," 2019.
J. Alotibi, B. Almutanni, T. Alsubait, H. Alhakami, A. J. I. J. o. A. C. S. Baz, and Applications, "Money Laundering Detection using Machine Learning and Deep Learning," vol. 13, no. 10, 2022.
Í. D. G. Silva, L. H. A. Correia, and E. G. Maziero, "Graph Neural Networks Applied to Money Laundering Detection in Intelligent Information Systems," in Proceedings of the XIX Brazilian Symposium on Information Systems, 2023, pp. 252-259.
J. Liu et al., "Graph embedding-based money laundering detection for Ethereum," vol. 12, no. 14, p. 3180, 2023.
M. Ça?layan and ?. J. G. U. J. o. S. Bahtiyar, "Money Laundering Detection with Node2Vec," vol. 35, no. 3, pp. 854-873, 2022.
G. Yang, X. Liu, B. J. C. Li, and Security, "Anti-money laundering supervision by intelligent algorithm," vol. 132, p. 103344, 2023.
N. Bakhshinejad, "A Graph-Based Deep Learning Model for Anti-Money Laundering," 2023.
T. Senior, "Enhancing Anti-Money Laundering Efforts with AI and ML A Comprehensive Approach to Financial Crime Prevention," 2021.
R. A. R. Mahmood, A. Abdi, and M. J. B. S. J. Hussin, "Performance evaluation of intrusion detection system using selected features and machine learning classifiers," vol. 18, no. 2 (Suppl.), pp. 0884-0884, 2021.
Z. J. B. S. J. Hussien, "Anomaly detection approach based on deep neural network and dropout," vol. 17, no. 2 (SI), pp. 0701-0701, 2020.
R.-H. Hwang, M.-C. Peng, C.-W. Huang, P.-C. Lin, and V.-L. J. I. A. Nguyen, "An unsupervised deep learning model for early network traffic anomaly detection," vol. 8, pp. 30387-30399, 2020.
W. Xu, J. Jang-Jaccard, A. Singh, Y. Wei, and F. J. I. A. Sabrina, "Improving performance of autoencoder-based network anomaly detection on nsl-kdd dataset," vol. 9, pp. 140136-140146, 2021.
Y.-C. Wang, Y.-C. Houng, H.-X. Chen, and S.-M. J. S. Tseng, "Network anomaly intrusion detection based on deep learning approach," vol. 23, no. 4, p. 2171, 2023.
L. Ashiku and C. J. P. C. S. Dagli, "Network intrusion detection system using deep learning," vol. 185, pp. 239-247, 2021.
P. Jing, Y. Gao, and X. J. a. p. a. Zeng, "A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation," 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Latha P, Thangaraj M
This work is licensed under a Creative Commons Attribution 4.0 International License.
CLEIej is supported by its home institution, CLEI, and by the contribution of the Latin American and international researchers community, and it does not apply any author charges whatsoever for submitting and publishing. Since its creation in 1998, all contents are made publicly accesibly. The current license being applied is a (CC)-BY license (effective October 2015; between 2011 and 2015 a (CC)-BY-NC license was used).
