EE226 Big Data Mining Project

Grade: The poster project takes 35-40% of your grade.

Team: You are expected to complete the project in a group of 2 students.

Report:

The final report should be complete with the following sections: introduction, related work, problem formulation, methods and findings, experiments, and conclusion. It is highly recommended to use the IEEE template for your report, and the expected length would be 5 pages. The report will be due on June 8th.

Presentation:

Please prepare slides for the in-class presentation on June 12th. Each group has 5 minutes to deliver a presentation of their project. The presentation will be evaluated depending on the correctness, completeness, clarity, and novelty of the project.

Proposal:

The proposal should be a one-page statement including: a clear definition of the problem you are trying to solve, background and existing literature, as well as your tentative solution. You are expected to deliver the proposal by April 20th (firm). We will provide feedback to your proposal by April 27th. The purpose is for us to provide critical judgment and suggestion to your project.

Please include the name and student number of each group member in the proposal. And submit it to oc.sjtu.edu.cn. We will provide the feedback to your sjtu email address that you registered on oc.sjtu.edu.cn.

Potential topics and recommended literature: You are free to select any topic in the scope of big data mining, not limited by the following possible topic list:

Learning with Your Smartphone

  1. S. Liu, Y. Lin, Z. Zhou, K. Nan, H. Liu, and J. Du. “On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Selection Framework.” In Proc. of the 16th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys),ACM, 2018.

  2. B. Fang, X. Zeng, and M. Zhang. “NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision.” In Proc. of the 24th Annual International Conference on Mobile Computing and Networking (MobiCom), pp. 115-127. ACM, 2018.

  3. Padmanabha Iyer, A., Erran Li, L., Chowdhury, M. and Stoica, I., 2018, October. “Mitigating the Latency-Accuracy Trade-off in Mobile Data Analytics Systems.” In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking (MobiCom), pp. 513-528. ACM.

  4. Huynh, L.N., Lee, Y. and Balan, R.K., 2017, June. “Deepmon: Mobile gpu-based deep learning framework for continuous vision applications.” In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys), pp. 82-95. ACM.

  5. Wang, J., Zhang, J., Bao, W., Zhu, X., Cao, B. and Yu, P.S., 2018, July. “Not just privacy: Improving performance of private deep learning in mobile cloud.” In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2407-2416). ACM.

Reinforcement Learning

  1. Khadka, S. and Tumer, K., 2018. “Evolution-Guided Policy Gradient in Reinforcement Learning.” In Advances in Neural Information Processing Systems (NIPS), pp. 1196-1208.

  2. Gupta, A., Mendonca, R., Liu, Y., Abbeel, P. and Levine, S., 2018. “Meta-reinforcement learning of structured exploration strategies.” In Advances in Neural Information Processing Systems (NIPS), pp. 5307-5316.

  3. Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, O.P. and Mordatch, I., 2017. “Multi-agent actor-critic for mixed cooperative-competitive environments.” In Advances in Neural Information Processing Systems (NIPS), pp. 6379-6390.

  4. Zoph, B. and Le, Q.V., 2017. “Neural Architecture Search with Reinforcement Learning.” In ICLR 2017.

Survey on Security and Privacy in Machine Learning

  1. Papernot, N., McDaniel, P., Sinha, A. and Wellman, M., 2016. “Towards the science of security and privacy in machine learning.” IEEE European Symposium on Security and Privacy (EuroS&P), 2018.

Adversarial Learning

  1. Madry, A., Makelov, A., Schmidt, L., Tsipras, D. and Vladu, A., 2018. “Towards deep learning models resistant to adversarial attacks.” In ICLR, 2018.

  2. Ma, X., Li, B., Wang, Y., Erfani, S.M., Wijewickrema, S., Schoenebeck, G., Song, D., Houle, M.E. and Bailey, J., 2018. “Characterizing adversarial subspaces using local intrinsic dimensionality.” In ICLR, 2018.

  3. Pei, K., Cao, Y., Yang, J. and Jana, S., 2017, October. “Deepxplore: Automated whitebox testing of deep learning systems.” In proceedings of the 26th Symposium on Operating Systems Principles (SOSP), pp. 1-18. ACM.

Attacks to Machine Learning

  1. Dosovitskiy, A. and Brox, T., 2016. “Inverting visual representations with convolutional networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4829-4837.

  2. Tramèr, F., Zhang, F., Juels, A., Reiter, M.K. and Ristenpart, T., 2016. “Stealing machine learning models via prediction apis.” In 25th USENIX Security Symposium (USENIX Security 16) (pp. 601-618).

  3. Shokri, R., Stronati, M., Song, C. and Shmatikov, V., 2017, May. “Membership inference attacks against machine learning models.” In 2017 IEEE Symposium on Security and Privacy (S&P), pp. 3-18. IEEE.

  4. Hitaj, B., Ateniese, G. and Perez-Cruz, F., 2017, October. “Deep models under the GAN: information leakage from collaborative deep learning.” In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 603-618. ACM.

  5. Salem, A., Zhang, Y., Humbert, M., Fritz, M. and Backes, M., 2018. “Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models.” In Proceedings of the 26th Annual Network and Distributed System Security Symposium (NDSS 2019)

  6. L.Melis, C.Song, E. De Cristofaro, V.Shmatikov. “Exploiting Unintended Feature Leakage in Collaborative Learning.” In 40th IEEE Symposium on Security and Privacy (S&P), 2019.

Differential Privacy

  1. M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang. “Deep Learning with Differential Privacy.” In Proc. of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 308-318. ACM, 2016.

  2. N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar. “Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data.” In Proc. of the 5th International Conference on Learning Representations (ICLR), 2017.

  3. N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Ú. Erlingsson. “Scalable Private Learning with PATE.” In Proc. of the 6th International Conference on Learning Representations (ICLR), 2018.

Cryptography and Machine Learning

  1. P. Mohassel, and Y. Zhang. “SecureML: A System for Scalable Privacy-Preserving Machine Learning.” In the 38th IEEE Symposium on Security and Privacy (S&P), pp. 19-38. IEEE, 2017.

  2. P. Mohassel, and P. Rindal. “ABY3: A Mixed Protocol Framework for Machine Learning.” In Proc. of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 35-52. ACM, 2018.