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.

We present a privacy-preserving deep learning system in which many learning participants perform neural network-based deep learning over a combined dataset of all, without revealing the participants' local data to a central server. To that end, we revisit the previous work by Shokri and Shmatikov (ACM CCS 2015) and show that, with their method, local data information may be leaked to an honest-but-curious server. We then fix that problem by building an enhanced system with the following properties: 1) no information is leaked to the server and 2) accuracy is kept intact, compared with that of the ordinary deep learning system also over the combined dataset. Our system bridges deep learning and cryptography: we utilize asynchronous stochastic gradient descent as applied to neural networks, in combination with additively homomorphic encryption. We show that our usage of encryption adds tolerable overhead to the ordinary deep learning system.

The amount of digital data that requires long-term protection of integrity, authenticity, and confidentiality grows rapidly. Examples include electronic health records, genome data, and tax data. In this paper we present the secure storage system LINCOS, which provides protection of integrity, authenticity, and confidentiality in the long-term, i.e., for an indefinite time period. It is the first such system. It uses the long-term integrity scheme COPRIS, which is also presented here and is the first such scheme that does not leak any information about the protected data. COPRIS uses information-theoretic hiding commitments for confidentiality-preserving integrity and authenticity protection. LINCOS uses proactive secret sharing for confidential storage of secret data. We also present implementations of COPRIS and LINCOS. A special feature of our LINCOS implementation is the use of quantum key distribution and one-time pad encryption for information-theoretic private channels within the proactive secret sharing protocol. The technological platform for this is the Tokyo QKD Network, which is one of worlds most advanced networks of its kind. Our experimental evaluation establishes the feasibility of LINCOS and shows that in view of the expected progress in quantum communication technology, LINCOS is a promising solution for protecting very sensitive data in the cloud.

PRINCESS (Proxy Re-encryption with INd-Cca security in an Encrypted file Storage System) is a secure storage system which utilizes special proxy re-encryption technology. With PRINCESS, the files encrypted in accordance with the confidentiality levels can be shared among appointed users while remaining encrypted. In this poster/demo, we show the efficiency of PRINCESS, which can be applied to a Body Area Network information sharing, automobile information sharing, etc. This system facilitates the potential for new services that require privacy data to be shared securely via cloud technology.