物理複製不能関数における安全性の評価と向上に関する研究

In this thesis, we focus on Physically Unclonable Functions (PUFs), which are expected as one of the most promising cryptographic primitives for secure chip authentication. Generally, PUFbased authentication is achieved by two approaches: (A) using a PUF itself, which has multiple challenge (input) and response (output) pairs, or (B) using a cryptographic function, the secret key of which is generated from a PUF with a single challenge-response pair (CRP). We contribute to:(1) evaluate the security of Approach (A), and (2) improve the security of Approach (B). (1) Arbiter-based PUFs were the most feasible type of PUFs, which was used to construct Approach (A). However, Arbiter-based PUFs have a vulnerability; if an attacker knows some CRPs, she/he can predict the remaining unknown CRPs with high probability. Bistable Ring PUF (BR-PUF) was proposed as an alternative, but has not been evaluated by third parties. In this thesis, in order to construct Approach (A) securely, we evaluate the difficulty of predicting responses of a BR-PUF experimentally. As a result, the same responses are frequently generated for two challenges with small Hamming distance. Also, particular bits of challenges have a great impact on the responses. In conclusion, BR-PUFs are not suitable for achieving Approach (A)securely. In future work, we should discuss an alternative PUF suitable for secure Approach (A).(2) In order to achieve Approach (B) securely, a secret key ? generated from a PUF response?should have high entropy. We propose a novel method of extracting high entropy from PUF responses. The core idea is to effectively utilize the information on the proportion of ‘1’s including in repeatedly-measured PUF responses. We evaluate its effectiveness by fabricated test chips. As a result, the extracted entropy is about 1.72 times as large as that without the proposed method.Finally, we organize newly gained knowledge in this thesis, and discuss a new application of PUF-based technologies.電気通信大学201

Sintering of hierarchically-structured boron carbide for toughening and multi- functionality

Boron carbide is light-weight, is thermally stable, has high hardness/stiffness, and is multi-functional (semiconducting, thermoelectric, and high neutron absorption cross-section). Boron carbide has been of interest for applications in extreme environments, including turbine engines, protection armor against impact, heat, and radiation, but such application is currently limited due to its brittleness and low sinterability. The toughening of ceramics has been investigated for many years as a light-weight, thermally/chemically stable alternative to structural materials. Among many methods, ceramic micro-fibers implementation has been effective, and further toughening is expected though engineering of matrices, specifically by implementing intentionally weak interphases to provide locally controlled deformation and thus energy dissipation within matrices. For example, in the past we experimentally studied the potentials of nano-porosity introduction into ceramics on deformation behaviors, by indenting on a model system of anodic aluminum oxide. Normally, porosity in ceramics is regarded as the defect, but we identified that, when pore size is below 100 nm, nanopores deform in a controlled manner (collapse or shear band, see Figure 1a), contributing to fracture toughness increase. Meanwhile, introduction of nano-porosity resulted in stiffness and hardness decrease. Please download the PDF file for full content

Deep Attention-based Representation Learning for Heart Sound Classification

Cardiovascular diseases are the leading cause of deaths and severely threaten human health in daily life. On the one hand, there have been dramatically increasing demands from both the clinical practice and the smart home application for monitoring the heart status of subjects suffering from chronic cardiovascular diseases. On the other hand, experienced physicians who can perform an efficient auscultation are still lacking in terms of number. Automatic heart sound classification leveraging the power of advanced signal processing and machine learning technologies has shown encouraging results. Nevertheless, human hand-crafted features are expensive and time-consuming. To this end, we propose a novel deep representation learning method with an attention mechanism for heart sound classification. In this paradigm, high-level representations are learnt automatically from the recorded heart sound data. Particularly, a global attention pooling layer improves the performance of the learnt representations by estimating the contribution of each unit in feature maps. The Heart Sounds Shenzhen (HSS) corpus (170 subjects involved) is used to validate the proposed method. Experimental results validate that, our approach can achieve an unweighted average recall of 51.2% for classifying three categories of heart sounds, i. e., normal, mild, and moderate/severe annotated by cardiologists with the help of Echocardiography