Visible to the public Biblio

Filters: Author is Moriai, Shiho  [Clear All Filters]
2020-08-07
Moriai, Shiho.  2019.  Privacy-Preserving Deep Learning via Additively Homomorphic Encryption. 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH). :198—198.

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.

2018-02-02
Braun, Johannes, Buchmann, Johannes, Demirel, Denise, Geihs, Matthias, Fujiwara, Mikio, Moriai, Shiho, Sasaki, Masahide, Waseda, Atsushi.  2017.  LINCOS: A Storage System Providing Long-Term Integrity, Authenticity, and Confidentiality. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. :461–468.
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.