There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

National Institute of Standards and Technology における超伝導研究及び生活

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.

Federated Machine Learning: Concept and Applications

Measuring Health: A Guide to Rating Scales and Questionnaires

Conductometric semiconducting metal oxide gas sensors have been widely used and investigated in the detection of gases. Investigations have indicated that the gas sensing process is strongly related to surface reactions, so one of the important parameters of gas sensors, the sensitivity of the metal oxide based materials, will change with the factors influencing the surface reactions, such as chemical components, surface-modification and microstructures of sensing layers, temperature and humidity. In this brief review, attention will be focused on changes of sensitivity of conductometric semiconducting metal oxide gas sensors due to the five factors mentioned above.

Metal oxide gas sensors: Sensitivity and influencing factors

We present a new garbled circuit construction for two-party secure function evaluation (SFE). In our one-round protocol, XOR gates are evaluated "for free", which results in the corresponding improvement over the best garbled circuit implementations (e.g. Fairplay [19]).

We build permutation networks [26] and Universal Circuits (UC) [25] almost exclusively of XOR gates; this results in a factor of up to 4 improvement (in both computation and communication) of their SFE. We also improve integer addition and equality testing by factor of up to 2.

We rely on the Random Oracle (RO) assumption. Our constructions are proven secure in the semi-honest model.

/pdf/improved-garbled-circuit-free-xor-gates-and-applications-3cn0m0a05t.pdf

Improved Garbled Circuit: Free XOR Gates and Applications

Secure multi-party computation has been considered by the cryptographic community for a number of years. Until recently it has been a purely theoretical area, with few implementations with which to test various ideas. This has led to a number of optimisations being proposed which are quite restricted in their application. In this paper we describe an implementation of the two-party case, using Yao's garbled circuits, and present various algorithmic protocol improvements. These optimisations are analysed both theoretically and empirically, using experiments of various adversarial situations. Our experimental data is provided for reasonably large circuits, including one which performs an AES encryption, a problem which we discuss in the context of various possible applications.

/pdf/secure-two-party-computation-is-practical-rqn7kar4pc.pdf

Secure Two-Party Computation Is Practical

from details of underlying secure computation protocol Use only fast symmetric key crypto Code is available on GitHub: http://encrypto.de/code/ABY

/pdf/aby-a-framework-for-efficient-mixed-protocol-secure-two-hozs81ospf.pdf

ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation

Secure two-party computation allows two untrusting parties to jointly compute an arbitrary function on their respective private inputs while revealing no information beyond the outcome. Existing cryptographic compilers can automatically generate secure computation protocols from high-level specifications, but are often limited in their use and efficiency of generated protocols as they are based on either garbled circuits or (additively) homomorphic encryption only.In this paper we present TASTY, a novel tool for automating, i.e., describing, generating, executing, benchmarking, and comparing, efficient secure two-party computation protocols. TASTY is a new compiler that can generate protocols based on homomorphic encryption and efficient garbled circuits as well as combinations of both, which often yields the most efficient protocols available today. The user provides a high-level description of the computations to be performed on encrypted data in a domain-specific language. This is automatically transformed into a protocol. TASTY provides most recent techniques and optimizations for practical secure two-party computation with low online latency. Moreover, it allows to efficiently evaluate circuits generated by the well-known Fairplay compiler.We use TASTY to compare protocols for secure multiplication based on homomorphic encryption with those based on garbled circuits and highly efficient Karatsuba multiplication. Further, we show how TASTY improves the online latency for securely evaluating the AES functionality by an order of magnitude compared to previous software implementations. TASTY allows to automatically generate efficient secure protocols for many privacy-preserving applications where we consider the use cases for private set intersection and face recognition protocols.

/pdf/tasty-tool-for-automating-secure-two-party-computations-4own82h6zz.pdf

TASTY: tool for automating secure two-party computations

Automatic recognition of human faces is becoming increasingly popular in civilian and law enforcement applications that require reliable recognition of humans. However, the rapid improvement and widespread deployment of this technology raises strong concerns regarding the violation of individuals' privacy. A typical application scenario for privacy-preserving face recognition concerns a client who privately searches for a specific face image in the face image database of a server.

In this paper we present a privacy-preserving face recognition scheme that substantially improves over previouswork in terms of communication-and computation efficiency: the most recent proposal of Erkin et al. (PETS'09) requires O(log M) rounds and computationally expensive operations on homomorphically encrypted data to recognize a face in a database of M faces. Our improved scheme requires only O(1) rounds and has a substantially smaller online communication complexity (by a factor of 15 for each database entry) and less computation complexity.

Our solution is based on known cryptographic building blocks combining homomorphic encryption with garbled circuits. Our implementation results show the practicality of our scheme also for large databases (e.g., for M = 1000 we need less than 13 seconds and less than 4 MByte on-line communication on two 2.4GHz PCs connected via Gigabit Ethernet).

/pdf/efficient-privacy-preserving-face-recognition-39562ez3ur.pdf

Thomas Schneider

Papers

Improved Garbled Circuit: Free XOR Gates and Applications

Secure Two-Party Computation Is Practical

ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation

TASTY: tool for automating secure two-party computations

Efficient privacy-preserving face recognition