1. Aono, Y., Hayashi, T., Wang, L., Moriai, S., et al. (2017). Privacy-preserving deep learning via additively homomorphic encryption. IEEE Transactions on Information Forensics and Security, 13(5), 1333–1345.
2. Banos, O., Garcia, R., Holgado-Terriza, J. A., Damas, M., Pomares, H., Rojas, I., Saez, A., & Villalonga, C. (2014). mHealthDroid: A novel framework for agile development of mobile health applications. In Pecchia, L., Chen, L. L., Nugent, C., & Bravo, J., (Eds.), Ambient assisted living and daily activities (pp. 91–98). Cham: Springer International Publishing.
3. Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., Van Overveldt, T., Petrou, D., Ramage, D., & Roselander, J. (2019). Towards Federated Learning at Scale: System Design. e-prints. arXiv:1902.01046.
4. Catak, F. O., Ahmed, J., Sahinbas, K., & Khand, Z. H. (2021). Data augmentation based malware detection using convolutional neural networks. PeerJ Computer Science, 7, e346.
5. Catak, F. O., Aydin, I., Elezaj, O., & Yildirim-Yayilgan, S. (2020). Practical implementation of privacy preserving clustering methods using a partially homomorphic encryption algorithm. Electronics, 9(2).