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New Directions in Efficient Privacypreserving Machine Learning

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New Directions in Efficient Privacypreserving Machine Learning New Directions in Efficient Privacy­Preserving Machine Learning Sameer Narahari Wagh May 12th, 2020 A Dissertation Presented to the Faculty of Princeton University in Candidacy for the Degree of Doctor of Philosophy Thesis Committee: Prof. Prateek Mittal (Advisor) . Princeton University Prof. Vincent Poor . .Princeton University Prof. Peter Ramadge . Princeton University Prof. Niraj Jha . Princeton University Dr. Nishanth Chandran . Microsoft Research, India © Copyright by Sameer Narahari Wagh, 2020. All rights reserved. To my Mother, for her unconditional love and unwavering belief in me. ii Abstract Applications of machine learning have become increasingly common in recent years. For instance, navigation systems like Google Maps use machine learning to better predict traffic patterns; Facebook, LinkedIn, and other social media platforms use machine learning to customize user’s news feeds. Central to all these systems is user data. However, the sensitive nature of the collected data has also led to a number of privacy concerns. Privacy-preserving machine learning enables systems that can perform such computation over sensitive data while protecting its privacy. In this dissertation, we focus on developing efficient protocols for machine learn- ing as a target analytics application. To incorporate privacy, we use a multi-party computation-based approach. In multi-party computation, a number of non-colluding entities jointly perform computation over the data and privacy stems from no party having any information about the data being computed on. At the heart of this disser- tation are three frameworks – SecureNN, Falcon, and Ponytail – each pushing the frontiers of privacy-preserving machine learning and propose novel approaches to protocol design. SecureNN and Falcon introduce, for the first time, highly effi- cient protocols for computation of non-linear functions (such as rectified linear unit, maxpool, batch-normalization) using purely modular arithmetic. Ponytail demon- strates the use of homomorphic encryption to significantly improve over prior art in private matrix multiplication. Each framework provides both significant asymptotic as well as concrete efficiency gains over prior work by improving computation aswell as communication performance by an order of magnitude. These building blocks – matrix multiplication, rectified linear unit, maxpool, batch-normalization – are central to machine learning and improvements to these significantly improve upon prior art in private machine learning. Furthermore, each of these systems is implemented and benchmarked to reduce the barrier of deploy- ment. Uniquely positioned at the intersection of both theory and practice, these frameworks bridge the gap between plaintext and privacy-preserving computation while contributing new directions for research to the community. iii Acknowledgments “Alone we can do so little; together we can do so much.” - Helen Keller Like any worthwhile undertaking, there is an entire universe of people that assist in the making of one PhD and this section is a tribute to my universe of people. I do not believe acknowledgments can be written in hindsight and true to this spirit, this part of the thesis was written over the entire course of my PhD. I see this as a reflection not of my PhD but about the people I shared it with and hence, toallof you, a big thank you for sharing parts of this journey with me! I strongly believe that researchers are made and not born and no sentiment could better acknowledge my advisor Prateek’s contribution in my life. I admire Prateek for his tremendous dedication, enthusiasm, and relentless optimism. I am grateful for his encouragement, for believing in me, and for all the creative freedom given to me during the course of my PhD. Working with Prateek has been one of the best decisions of my life and if I ever were to relive these years, I would make this decision every single time! I would also like to acknowledge Paul Cuff for his instrumental role in shaping me as a student, scholar, and as an individual. Paul has been like a second advisor to me, a close mentor for life, as well as a wonderful basketball colleague. A special acknowledgment to Prof. Rajesh Narayanan, Prof. Nic Shannon, Prof David Dorfan, and M Prakash Sir, my previous advisors and mentors, who introduced me to the path of research, provided invaluable mentorship, and without whom I would never have chosen this path in life. I would like to thank my thesis committee – Prof. Niraj Jha and Nishanth Chandran as readers, Prof. Vince Poor and Prof. Peter Ramadge as the examiners – as well as other department faculties that I had the opportunity to interact with during my time at Princeton – Prof. Paul Prucnal and Prof. Alejandro Rodriguez. A special acknowledgment to my mentors Nishanth Chandran (doubly!), Hao Chen, Ilya Razenshteyn, Divya Gupta, Tal Rabin, Olya Ohrimenko, Benny Pinkas, and Assi Barak. They have been instrumental in my professional development and without whom I would not be where I am today. I would also like to thank all my collaborators Miran Kim, Yongsoo Song, Fabrice Benhamouda, Eyal Kushilevitz, Xi He, Ashwin Machanavajjhala, Aaron Johnson, Ryan Wails, Lawrence Esswood, Felix Schuster, Manuel Costa, Yanqi Zhou, and David Wentzlaff. Dragoş and Saeed, a very special thanks to both of you, Ponytail would not be half as fun without you. Likewise, Hans and Gerry, it has been a pleasure working with you and I wish you the very best of luck in your journey at Grad school. I would also like to thank all the awards and grants that have supported the research efforts of this dissertation. Lisa, fortunately or unfortunately, you had an office right opposite Prateek’s and had to see me often. No amount of words can thank you – from simple favors, to elaborate requests, you have helped me through it all and I cannot imagine my Princeton journey without you. No matter where I am, you will keep receiving post- cards from around the world. A big thank you to all the incredible ELE departmental iv staff Colleen, Roelie, Dorothy, Heather, Stacey, Kate, Jean, Lidia, and Abby who truly form the backbone of the department. A special thank you to Lori, Katie, and Muhamed for all the support in making my graduate life easier and to Cecilia for all her support in organizing all kinds of departmental events – MelodEE, the departmental t-shirt and more. I sincerely applaud your dedication and efforts to make our department such a fun place. I would like to express my deepest appreciation for some people who have not only shared this journey with me but also made this journey what it is. First and foremost, my deepest gratitude to Rutwik, a dear friend and long-time roommate; I truly cannot imagine my journey at Princeton without you. I have learned so much from you and the countless memories we have together shall be cherished forever. I look forward to more wonderful times together. To Shruti Tople, a dear friend, a trustworthy guide, and a fantastic collaborator. To Sergiy for being a humble link to JG and ELDOA but more importantly for just being yourself. To John-Garret a.k.a JG, for being the best body mechanic on this planet, and for showing me the way to living my life more fully. To Luciano, for all the fun times we shared together and many more to come. To Yeohee, for the fun jamming for MelodEE and for all that I have learned from you. To Tri and Mayank for all the crazy conversations and more than making up for the Ph in a PhD. A special appreciation for my Yoga instructor David as well as all my yoga friends. To my host family Rona, Rob, Kate and Matt, Sara and Bernat who graciously welcomed me not just into the United States but also into their lives. To Brett Bishop, for the enjoyable workouts together and for all your honest inputs on life. To Yan, for being a phenomenal mentor as well as a wonderful book guru. To Lanqing and Jonathan for all the fun puzzles over lunch and otherwise and to Prakhar, Vikash, Shashank, Siddharth, Shaurya, Pranav, Chris, Uno, Wen, Theresa, and Vitor for all the cooking and meals we shared. Finally, Sandra, Hamid, Mina, and Sepehr, you guys have been an instrumental part of my Princeton lives and I will cherish every moment we spent together! I would also like to thank all my friends across the world, especially from India; you made this journey a real pleasure and you’re all fondly remembered during the writing of this acknowledgment. A special and heartfelt appreciation to Harshad Chandak for everything (in particular for all the “making memories”). My sincere gratitude to all my family in India as well as in the States – without your support, this journey would not be easy. I would like to acknowledge a few of my friends, in and outside Princeton for being a part of this journey: Changchang, Peng, Yixin, Yushan, Thee, Liwei, Arjun, Hooman, Tong, Shivam, Irineo, Vera, Nadeen, Molly, Quinn, Abhishek, Shweta, and Sukrit, Sapad, Daniel and Christina, Anders, Harsh, Rahul, Shir, Eysa, and Eduardo, Mohammad Samragh, Sadegh (MSR), Ilia, Lynn, and Pardis. I would also like to thank Anita (and Zalalem) for being one of the best hosts on this planet. I thank numerous people I have played badminton, basketball, ultimate, and soccer with, in particular, my folks and coaches at CFN and the FCRangers crew for the wonderful soccer time in Seattle. I would like to thank all the organizations and grants that have supported my work. Finally, I would like to thank Princeton for giving me this wonderful environment to thrive to the best of my abilities, an entire ecosystem of talented individuals to learn from. v Contents Abstract ..................................... iii Acknowledgments ...............................
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