Biblio

We aim at creating a society where we can resolve various social challenges by incorporating the innovations of the fourth industrial revolution (e.g. IoT, big data, AI, robot, and the sharing economy) into every industry and social life. By doing so the society of the future will be one in which new values and services are created continuously, making people's lives more conformable and sustainable. This is Society 5.0, a super-smart society. Security and privacy are key issues to be addressed to realize Society 5.0. Privacy-preserving data analytics will play an important role. In this talk we show our recent works on privacy-preserving data analytics such as privacy-preserving logistic regression and privacy-preserving deep learning. Finally, we show our ongoing research project under JST CREST “AI”. In this project we are developing privacy-preserving financial data analytics systems that can detect fraud with high security and accuracy. To validate the systems, we will perform demonstration tests with several financial institutions and solve the problems necessary for their implementation in the real world.

This paper presents DeepSecure, the an scalable and provably secure Deep Learning (DL) framework that is built upon automated design, efficient logic synthesis, and optimization methodologies. DeepSecure targets scenarios in which neither of the involved parties including the cloud servers that hold the DL model parameters or the delegating clients who own the data is willing to reveal their information. Our framework is the first to empower accurate and scalable DL analysis of data generated by distributed clients without sacrificing the security to maintain efficiency. The secure DL computation in DeepSecure is performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized realization of various components used in DL. Our optimized implementation achieves up to 58-fold higher throughput per sample compared with the best prior solution. In addition to the optimized GC realization, we introduce a set of novel low-overhead pre-processing techniques which further reduce the GC overall runtime in the context of DL. Our extensive evaluations demonstrate up to two orders-of-magnitude additional runtime improvement achieved as a result of our pre-processing methodology.

We provide a systemization of knowledge of the recent progress made in addressing the crucial problem of deep learning on encrypted data. The problem is important due to the prevalence of deep learning models across various applications, and privacy concerns over the exposure of deep learning IP and user's data. Our focus is on provably secure methodologies that rely on cryptographic primitives and not trusted third parties/platforms. Computational intensity of the learning models, together with the complexity of realization of the cryptography algorithms hinder the practical implementation a challenge. We provide a summary of the state-of-the-art, comparison of the existing solutions, as well as future challenges and opportunities.