Masami Izumi

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In 2008, Izu et al. introduced a concept of the sanitizable and deletable signature as a combination of the sanitizable signature and the deletable signature in which the partial information (e.g. privacy information) can be sanitized or deleted with keeping the integrity of disclosed parts. They also constructed two concrete sanitizable and deletable(More)
High speed DSP blocks present in the modern FPGAs can be used to implement prime field multiplication to accelerate Elliptic Curve scalar multiplication in prime fields. However, compared to logic slices, DSP blocks are scarce resources, hence its usage needs to be optimized. The asymmetric 25 × 18 signed multipliers in FPGAs open a new paradigm for(More)
Messerges, Dabbish and Sloan proposed a DPA attack which analyzes the address values of registers [1]. This attack is called the Address-bit DPA (ADPA) attack. As countermeasures against ADPA, Itoh, Izu and Takenaka proposed algorithms that randomizes address bits [2]. In this paper, we point out that one of their countermeasures has vulnerability even if(More)
Recent years, the information security is emphasized with a development of Internet systems. In the measures as securing digital information, there are cryptosystems that protect secrecy of digital documents and digital signature scheme that ensure validity of digital documents. In the case of reality, i.e. hardware devices are used in cryptosystems, there(More)
In this investigation, we propose a sophisticated approach for estimating the ages of blog authors by means of stochastic process. In this technique, we give weights on every word appeared in training data, and we extract a collection of feature words to each age. Then we examine articles on Blog based on the feature information and estimate the age by(More)
In this investigation, we propose an experimental approach for word segmentation in Japanese under domain-dependent situation. We apply Conditional Random Fields (CRF) to our issue. CRF learns several probabilistic parameters from training data with specific feature functions dependent on domains. Here we propose how to define domain specific feature(More)
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