Sebastian Hasler

Abstract

Machine learning (ML) has seen a strong rise in popularity in recent years and has become an essential tool for research and industrial applications. Given the large amount of high quality data needed and the often sensitive nature of ML data, privacy-preserving collaborative ML is of increasing importance. In this paper, we introduce new actively secure multiparty computation (MPC) protocols which are specially optimized for privacy-preserving machine learning applications. We concentrate on the optimization of (tensor) convolutions which belong to the most commonly used components in ML architectures, especially in convolutional neural networks but also in recurrent neural networks or transformers, and therefore have a major impact on the overall performance. Our approach is based on a generalized form of structured randomness that speeds up convolutions in a fast online phase. The structured randomness is generated with homomorphic encryption using adapted and newly constructed packing methods for convolutions, which might be of independent interest. Overall our protocols extend the state-of-the-art Overdrive family of protocols (Keller et al., EUROCRYPT 2018). We implemented our protocols on-top of MP-SPDZ (Keller, CCS 2020) resulting in a full-featured implementation with support for faster convolutions. Our evaluation shows that our protocols outperform state-of-the-art actively secure MPC protocols on ML tasks like evaluating ResNet50 by a factor of 3 or more. Benchmarks for depthwise convolutions show order-of-magnitude speed-ups compared to existing approaches.

Abstract

Machine learning (ML) has seen a strong rise in popularity in recent years and has become an essential tool for research and industrial applications. Given the large amount of high quality data needed and the often sensitive nature of ML data, privacy-preserving collaborative ML is of increasing importance. In this paper, we introduce new actively secure multiparty computation (MPC) protocols which are specially optimized for privacy-preserving machine learning applications. We concentrate on the optimization of (tensor) convolutions which belong to the most commonly used components in ML architectures, especially in convolutional neural networks but also in recurrent neural networks or transformers, and therefore have a major impact on the overall performance. Our approach is based on a generalized form of structured randomness that speeds up convolutions in a fast online phase. The structured randomness is generated with homomorphic encryption using adapted and newly constructed packing methods for convolutions, which might be of independent interest. Overall our protocols extend the state-of-the-art Overdrive family of protocols (Keller et al., EUROCRYPT 2018). We implemented our protocols on-top of MP-SPDZ (Keller, CCS 2020) resulting in a full-featured implementation with support for faster convolutions. Our evaluation shows that our protocols outperform state-of-the-art actively secure MPC protocols on ML tasks like evaluating ResNet50 by a factor of 3 or more. Benchmarks for depthwise convolutions show order-of-magnitude speed-ups compared to existing approaches.

Abstract

Some of the most efficient protocols for Multi-Party Computation (MPC) follow a two-phase approach where correlated randomness, in particular Beaver triples, is generated in the offline phase and then used to speed up the online phase. Recently, more complex correlations have been introduced to optimize certain operations even further, such as matrix triples for matrix multiplications. In this paper, our goal is to improve the efficiency of the triple generation in general and in particular for classical field values as well as matrix operations. To this end, we modify the Overdrive LowGear protocol to remove the costly sacrificing step and therewith reduce the round complexity and the bandwidth. We extend the state-of- the-art MP-SPDZ implementation with our new protocols and show that the new offline phase outperforms state-of-the-art protocols for the generation of Beaver triples and matrix triples. For example, we save 33 % in bandwidth compared to Overdrive LowGear.

Abstract

Test methods that can keep up with the ongoing increase in complexity of semiconductor products and their underlying technologies are an essential prerequisite for maintaining quality and safety of our daily lives and for continued success of our economies and societies. There is a huge potential how test methods can benefit from recent breakthroughs in domains such as artificial intelligence, data analytics, virtual/augmented reality, and security. The Graduate School on “Intelligent Methods for Semiconductor Test and Reliability” (GS-IMTR) at the University of Stuttgart is a large-scale, radically interdisciplinary effort to address the scientific-technological challenges in this domain. It is funded by Advantest, one of the world leaders in automatic test equipment. In this paper, we describe the overall philosophy of the Graduate School and the specific scientific questions targeted by its ten projects.