TALK: Training Machine Learning Models with Private Data

All dates for this event occur in the past.

The OSU AIoT seminar series will resume this Friday, Sept. 22. 


Date and Time: Friday, Sept. 22-  1pm - 2pm EST, online


Invited Speaker: Murali Annavaram, Lloyd Hunt Chair Professor, University of Southern California


Zoom Link:

Meeting ID: 982 4773 6070

Password: 692193 

Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train or infer with private data while exploiting accelerators, such as GPUs, that are hosted in the cloud. Cloud systems are vulnerable to attackers that compromise the privacy of data and integrity of computations. Tackling such a challenge efficiently requires exploiting hardware security capabilities to reduce the cost of theoretical privacy algorithms. This talk will describe my group’s experiences in building privacy preserving machine learning systems. I will present DarKnight, a framework for large DNN training while protecting input privacy and computation integrity. DarKnight relies on cooperative execution between trusted execution environments (TEE) and accelerators, where the TEE provides privacy and integrity verification, while accelerators perform the bulk of the linear algebraic computation to optimize the performance. The second part of the talk will focus on an orthogonal approach to privacy using multi-party computing (MPC). We present detailed characterisation of MPC overheads when executing large language models in a distributed manner. We then present MPCpipe a pipelined MPC execution model that overlaps computation and communication in MPC.

Murali Annavaram is the Lloyd Hunt Chair Professor in the Ming-Hsieh Department of Electrical and Computer Engineering and in the Thomas Lord department of Computer Science (joint appointment) at the University of Southern California. He was the Rukmini Gopalakrishnachar Chair Professor at the Indian Institute of Science. He is the founding director of the REAL@USC-Meta center that is focused on research and education in AI and learning. His research group tackles a wide range of computer system design challenges, relating to energy efficiency, security and privacy. He has been inducted to the hall of fame for three of the prestigious computer architecture conferences ISCA, MICRO and HPCA. He served as a Technical Program Chair for HPCA 2021, and served as the General Co-Chair for ISCA 2018. Prior to his appointment at USC he worked at Intel Microprocessor Research Labs from 2001 to 2007. His work at Intel lead to the first 3D microarchitecture paper, and also influenced Intel’s TurboBoost technology. In 2007 he was a visiting researcher at the Nokia Research Center working on mobile phone-based wireless traffic sensing using virtual trip lines, which later become Nokia Traffic Works product. In 2020 he was a visiting faculty scientist at Facebook, where he designed the checkpoint systems for distributed training. Murali co-authored Parallel Computer Organization and Design, a widely used textbook to teach both the basic and advanced principles of computer architecture. Murali received the Ph.D. degree in Computer Engineering from the University of Michigan, Ann Arbor, in 2001. He is a Fellow of IEEE and Senior Member of ACM.