Unveiling the Veiled: Leveraging Deep Learning and Network Analysis for De-Anonymization in Social Networks

Rutba-Aman^1*, Rahnuma Tasmin¹, Poly Rani Ghosh¹

Online anonymity enables free expression but raises concerns like cyberbullying; de-anonymization techniques aim to balance safety and openness online.

Abstract

Online anonymity allows individuals to safeguard their personal information. It provides the freedom for anyone to express themselves without being concerned about censorship, discrimination, or retaliation. This pseudonym also provides opportunities for individuals to promote open discourse and diverse viewpoints. However, in today's digital age, this online anonymity has become a growing concern. Although the safeguarding of people’s rights, the advancement of free speech, and the development of a more diverse and democratic online community all depend heavily on the anonymity of online users, there are risks as well, such as cyberbullying, harassment, and the propagation of false information. Because of their anonymity, predators, groomers, and other unscrupulous individuals may be able to take advantage of vulnerable individuals, especially children and adolescents. In order to trick victims into hazardous or abusive situations, adversaries can hide their identities. Deep learning and network analysis can be used to reveal the true identities of anonymous social media users in order to combat this. Deep learning algorithms are capable of analyzing a wide range of social network data, including user behavior, relationships, and content, in order to find patterns and correlations that can lead to the true identities of users that go anonymous. The proposed system includes examining network topology and group dynamics to identify potential anomalies and connections that could lead to de-anonymization. This paper proposes a novel module for de-anonymization integrating deep learning and network analysis in social networks.

Keywords: online anonymity, free speech, cyberbullying, de-anonymization, social networks.

References

Arachchige, P. C. M., Bertok, P., Khalil, I., Liu, D., Camtepe, S., & Atiquzzaman, M. (2019). Local differential privacy for deep learning. IEEE Internet of Things Journal, 7(7), 5827-5842.

Basak, D., Pal, S., & Dutta, P. (2021). Financial time series forecasting using deep learning: A systematic review and bibliometric analysis. Applied Soft Computing, 108, 107515.

Deanonymization: Blurring the Boundaries of Social Network Privacy. (n.d.). Retrieved from https://fastercapital.com/content/Deanonymization--Blurring-the-Boundaries-of-Social-Network-Privacy.html

Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. Foundations and Trends® in Signal Processing, 7(3–4), 197-387.

Fu, X., Hu, Z., Xu, Z., Fu, L., & Wang, X. (2017, December). De-anonymization of networks with communities: Analysis, algorithm and experiments. In GLOBECOM 2017-2017 IEEE Global Communications Conference (pp. 1-6). IEEE.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144.

Gross, R., & Acquisti, A. (2005, November). Information revelation and privacy in online social networks. In Proceedings of the 2005 ACM workshop on Privacy in the electronic society (pp. 71-80).

Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., ... & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354-377.

Havinga, I., Marcos, D., Bogaart, P., Massimino, D., Hein, L., & Tuia, D. (2023). Deep learning in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 185, 290-307.

Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82-97.

Hsu, T. S., Liau, C. J., & Wang, D. W. (2014). A logical framework for privacy-preserving social network publication. Journal of Applied Logic, 12(2), 151-174.

Introduction To Deanonymization And Encryption. (n.d.). Retrieved from https://fastercapital.com/topics/introduction-to-deanonymization-and-encryption.html

Jiang, H., Gao, Y., Sarwar, S. M., GarzaPerez, L., & Robin, M. (2021, December). Differential privacy in privacy-preserving big data and learning: Challenge and opportunity. In Silicon Valley Cybersecurity Conference (pp. 33-44). Cham: Springer International Publishing.

Jiang, H., Pei, J., Yu, D., Yu, J., Gong, B., & Cheng, X. (2021). Applications of differential privacy in social network analysis: A survey. IEEE Transactions on Knowledge and Data Engineering, 35(1), 108-127.

Jiang, H., Yu, J., Cheng, X., Zhang, C., Gong, B., & Yu, H. (2021). Structure-attribute-based social network deanonymization with spectral graph partitioning. IEEE Transactions on Computational Social Systems, 9(3), 902-913.

Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 1-22.

Korolova, A., Motwani, R., Nabar, S. U., & Xu, Y. (2008, October). Link privacy in social networks. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (pp. 289-298).

Kourtis, M. A., Oikonomakis, A., Papadopoulos, D., Xylouris, G., & Chochliouros, I. P. (2021, December). Leveraging Deep Learning for Network Anomaly Detection. In 2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC) (pp. 1-6). IEEE.

Kumar, G., & Kumar, K. (2013). Design of an evolutionary approach for intrusion detection. The Scientific World Journal, 2013.

Lee, W. H., Liu, C., Ji, S., Mittal, P., & Lee, R. B. (2017, October). Blind de-anonymization attacks using social networks. In Proceedings of the 2017 on Workshop on Privacy in the Electronic Society (pp. 1-4).

Li, L., Yang, J., & Dong, J. (2023). Big data analytics in finance: A systematic review and agenda for future research. European Journal of Operational Research, 296(1), 113-129.

Li, X., Garg, S., & Iorga, M. (2021). Deep learning for remote sensing image classification: A survey. Remote Sensing, 13(13), 2475.

Lin, J., Dai, W., Zhao, B., & Wang, S. (2023). Deep learning applications in finance and accounting: A systematic review. International Journal of Accounting Information Systems, 41, 100580.

Liu, L., Wang, J., Liu, J., & Zhang, J. (2008). Privacy preserving in social networks against sensitive edge disclosure. Technical Report Technical Report CMIDA-HiPSCCS 006-08, Department of Computer Science, University of Kentucky, KY.

Luceri, L., Braun, T., & Giordano, S. (2019). Analyzing and inferring human real-life behavior through online social networks with social influence deep learning. Applied Network Science, 4(1), 1-25.

Ma, C., Du, X., & Cao, L. (2019). Analysis of multi-types of flow features based on hybrid neural network for improving network anomaly detection. IEEE Access, 7, 148363-148380.

Mondal, M., Correa, D., & Benevenuto, F. (2020, July). Anonymity effects: A large-scale dataset from an anonymous social media platform. In Proceedings of the 31st ACM Conference on Hypertext and Social Media (pp. 69-74).

Narayanan, A., & Shmatikov, V. (2009). De-anonymizing Social Networks. arXiv preprint arXiv:0903.3276.

Narayanan, A., & Shmatikov, V. (2009). De-anonymizing Social Networks. arXiv preprint arXiv:0903.3276.

Narayanan, A., & Shmatikov, V. (2009, May). De-anonymizing social networks. In 2009 30th IEEE Symposium on Security and Privacy (pp. 173-187). IEEE.

Qian, J., Li, X. Y., Jung, T., Fan, Y., Wang, Y., & Tang, S. (2019). Social network de-anonymization: More adversarial knowledge, more users re-identified?. ACM Transactions on Internet Technology (TOIT), 19(3), 1-22.

Qureshi, R., & Iftekharuddin, K. M. (2021). Recent advancements in deep learning architectures for multi-modal data: A survey. Pattern Recognition Letters, 150, 67-76.

Razavi-Far, R., Ruiz-Garcia, A., Palade, V., & Schmidhuber, J. (Eds.). (2022). Generative adversarial learning: architectures and applications. Springer International Publishing.

Retrieved from https://kdd.org/kdd2023/wp-content/uploads/2023/08/toc.html

Sharad, K. (2016). Learning to de-anonymize social networks (No. UCAM-CL-TR-896). University of Cambridge, Computer Laboratory.

Shrestha, R., Santucci, G., Gautam, S., & Keogh, E. (2021). Deep learning techniques for time series forecasting: A review of advancements and applications. Big Data Research, 24, 100221.

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

Sopuru, J., Sari, A., & Akkaya, M. (2019). Modeling A malware detection and categorization system based on seven network flow-based features. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(7).

Tadesse, T., & Reddy, K. (2021). Deep learning applications in agriculture: A review. Journal of King Saud University-Computer and Information Sciences.

Thakur, N., & Kumar, A. (2022). Deep learning for remote sensing: A comprehensive survey. Computers & Electrical Engineering, 100, 107313.

Tiwari, A., Majumder, P., & Mishra, H. (2022). Deep learning applications in agriculture: A comprehensive review. Computers and Electronics in Agriculture, 195, 105369.

Vasconcelos, M., de Castro, R., Azevedo, H. C., & Costa, H. (2022). A review on the application of deep learning in precision agriculture. Computers and Electronics in Agriculture, 195, 105308.

Wang, W., Sheng, Y., Wang, J., Zeng, X., Ye, X., Huang, Y., & Zhu, M. (2017). HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access, 6, 1792-1806.

Wang, W., Zhu, M., Zeng, X., Ye, X., & Sheng, Y. (2017, January). Malware traffic classification using convolutional neural network for representation learning. In 2017 International Conference on Information Networking (ICOIN) (pp. 712-717). IEEE.

Wang, Y., Yan, L., Liu, Y., Li, Z., Huang, Y., & Qu, Y. (2021). An image-based deep learning framework for crop yield prediction. Computers and Electronics in Agriculture, 184, 106145.

Wondracek, G., Holz, T., Kirda, E., & Kruegel, C. (2010, May). A practical attack to de-anonymize social network users. In 2010 IEEE Symposium on Security and Privacy (pp. 223-238). IEEE.

Wondracek, G., Holz, T., Kirda, E., & Kruegel, C. (2010, May). A practical attack to de-anonymize social network users. In 2010 IEEE Symposium on Security and Privacy (pp. 223-238). IEEE.

Xiao, F., & Li, X. (2022). Deep learning for remote sensing image classification: A comprehensive review. Remote Sensing, 14(4), 705.

Xie, Y., & Zheng, M. (2016). A differentiated anonymity algorithm for social network privacy preservation. Algorithms, 9(4), 85.

Xu, Y., Meng, X., Li, Y., & Xu, X. (2020). Research on privacy disclosure detection method in social networks based on multi-dimensional deep learning. Comput. Mater. Contin, 62, 137-155.

Ying, X., & Wu, X. (2011). On link privacy in randomizing social networks. Knowledge and Information Systems, 28, 645-663.