Privacy-Preserving in the Context of Data Mining and Deep Learning |
Altalhi, Amjaad
(Department of Computer Science, College of Computers and Information Technology, Taif University)
AL-Saedi, Maram (Department of Computer Science, College of Computers and Information Technology, Taif University) Alsuwat, Hatim (Department of Computer Science, College of Computer and Information Systems, Umm Al Qura University) Alsuwat, Emad (Department of Computer Science, College of Computers and Information Technology, Taif University) |
1 | A. C. Yao, "Protocols for secure computations," 23rd Annu. Symp. Found. Comput. Sci. (secs 1982), pp. 1-5, 1982. |
2 | H. C. Tanuwidjaja, R. Choi, S. Baek, and K. Kim, "Privacy-Preserving Deep Learning on Machine Learning as a Service-a Comprehensive Survey," in IEEE Access, vol. 8, pp. 167425-167447, 2020, DOI: 10.1109/ACCESS.2020.3023084. DOI |
3 | J. Vaidya, B. Shafiq, W. Fan, D. Mehmood, and D. Lorenzi, "A Random Decision Tree Framework for Privacy-Preserving Data Mining," IEEE Trans. Dependable Secure. Comput., vol. 11, no. 5, pp. 399-411, 2014. DOI |
4 | Wu, B., Chen, C., Wang, L., Wang, L., Tan, J., Chen, C., ... & Sun, G. (2020). Poster: Nebula: an Industrial-purpose Privacy-preserving Machine Learning System. In 2020 IEEE Symposium on Security and Privacy (SP). IEEE. |
5 | Raynal, M., Achanta, R., & Humbert, M. (2020). Image Obfuscation for Privacy-Preserving Machine Learning. arXiv preprint arXiv:2010.10139. |
6 | Kaissis, G. A., Makowski, M. R., Ruckert, D., & Braren, R. F. (2020). Secure, privacy-preserving, and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305-311 DOI |
7 | A. Narayanan and V. Shmatikov. Robust de-anonymization of large sparse datasets. In IEEE Symposium on Security and Privacy, pages 111-125, 2008. |
8 | A Patra, A Suresh - arXiv preprint arXiv:BLAZE: Blazing Fast Privacy-Preserving Machine Learning2005.09042, 2020 - arxiv.org |
9 | C. Dwork, G. N. Rothblum, and S. Vadhan, "Boosting and Differential Privacy," 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, Las Vegas, NV, 2010, pp. 51- 60. |
10 | O. Goldreich, S. Micali, and A. Wigderson, "How to Play any Mental Game," Stoc '87, pp. 218-229, 1987 |
11 | Gaur, M. (2020). Privacy-Preserving Machine Learning Challenges and Solution Approach for Training Data in ERP Systems. International Journal of Computer Engineering and Technology. |
12 | Reddy, S. M., & Miriyala, S. (2020). Security and privacy-preserving deep learning. arXiv preprint arXiv:2006.12698. |
13 | Tanuwidjaja, H. C., Choi, R., Baek, S., & Kim, K. (2020). Privacy-Preserving Deep Learning on Machine Learning as a Service-a Comprehensive Survey. IEEE Access, 8, 167425-167447. DOI |
14 | P. C. M. Arachchige, P. Bertok, I. Khalil, D. Liu, S. Camtepe and M. Atiquzzaman, "A Trustworthy Privacy-Preserving Framework for Machine Learning in Industrial IoT Systems," in IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 6092-6102, Sept. 2020, DOI: 10.1109/TII.2020.2974555 DOI |
15 | C. Dwork, G. J. Pappas, "Privacy in information-rich intelligent infrastructure," 2017. |
16 | Carlini, Nicholas, et al., The Secret Sharer: Evaluating and testing unintended memorization in neural networks (2019), 28th USENIX Security Symposium (USENIX Security 19) |
17 | R. Agrawal and R. Srikant, "Privacy-preserving data mining," Proc. 2000 ACM SIGMOD Int. Conf. Manag. data - SIGMOD '00, vol. 29, no. 2, pp. 439-450, 2000 |
18 | Netflixchallenge-https://dl.acm.org/doi/10.1145/1345448.1345465 |
19 | Xu, K., Yue, H., Guo, L., Guo, Y., & Fang, Y. (2015, June). Privacy-preserving machine learning algorithms for extensive data systems. In 2015 IEEE 35th international conference on distributed computing systems (pp. 318-327). IEEE. |
20 | Rachuri, R., & Suresh, A. (2019). Trident: efficient 4PC framework for privacy-preserving machine learning. arXiv preprint arXiv:1912.02631. |
21 | The IEEE computer society, 2004, "Privacy-Preserving Data Mining: Why, How, and When." |
22 | Boulemtafes, A., Derhab, A., & Challal, Y. (2020). A review of privacy-preserving techniques for deep learning. Neurocomputing, 384, 21-45. DOI |
23 | Behler, J. (2016). Perspective: Machine learning potentials for atomistic simulations. The Journal of chemical physics, 145(17), 170901. DOI |
24 | M. Rajarajan, K. Cumanan, S. Veluru, R. C.-W. Phan, and Y. Rahulamathavan, "Privacy-Preserving Multi-Class Support Vector Machine for Outsourcing the Data Classification in Cloud," IEEE Trans. Dependable Secure. Comput., vol. 11, no. 5, pp. 467-479, 2013. |
25 | Mahesh Dhande, N.A.Nemade and Yogesh Kolhe, 2013, "Privacy Preserving in K- Anonymization Databases Using AES Technique" |