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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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

With the establishment of the AES the need for new block ciphers has been greatly diminished; for almost all block cipher applications the AES is an excellent and preferred choice. However, despite recent implementation advances, the AES is not suitable for extremely constrained environments such as RFID tags and sensor networks. In this paper we describe an ultra-lightweight block cipher, present . Both security and hardware efficiency have been equally important during the design of the cipher and at 1570 GE, the hardware requirements for present are competitive with today's leading compact stream ciphers.

/pdf/present-an-ultra-lightweight-block-cipher-33ym33ql7r.pdf

PRESENT: An Ultra-Lightweight Block Cipher

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Recently, a lot of progress has been made in the implementation of pairings in both hardware and software. In this paper, we present two FPGA-based high speed pairing designs using the Residue Number System and lazy reduction. We show that by combining RNS, which is naturally suitable for parallel architectures, and lazy reduction, which performs one reduction for multiple multiplications, the speed of pairing computation in hardware can be largely increased. The results show that both designs achieve higher speed than previous designs. The fastest version computes an optimal ate pairing at 126-bit security level in 0.573 ms, which is 2 times faster than all previous hardware implementations at the same security level.

/pdf/fpga-implementation-of-pairings-using-residue-number-system-2sfbt27lex.pdf

FPGA implementation of pairings using residue number system and lazy reduction

We present SFINKS, a low-cost synchronous stream cipher for hardware applications with an associated authentication mechanism. The stream cipher is based on a Simple Filter generator, using the INverse function in F216 to generate the Key Stream. The design is based on simple and well-studied concepts, and its security is analyzed with respect to the portfolio of known cryptanalytic attacks for filter generators.

SFINKS: A synchronous stream cipher for restricted hardware environments

This paper proposes two improved interleaved modular multiplication algorithms based on Barrett and Montgomery modular reduction. The algorithms are simple and especially suitable for hardware implementations. Four large sets of moduli for which the proposed methods apply are given and analyzed from a security point of view. By considering state-of-the-art attacks on public-key cryptosystems, we show that the proposed sets are safe to use, in practice, for both elliptic curve cryptography and RSA cryptosystems. We propose a hardware architecture for the modular multiplier that is based on our methods. The results show that concerning the speed, our proposed architecture outperforms the modular multiplier based on standard modular multiplication by more than 50 percent. Additionally, our design consumes less area compared to the standard solutions.

/pdf/faster-interleaved-modular-multiplication-based-on-barrett-2yvrc9frrg.pdf

Faster Interleaved Modular Multiplication Based on Barrett and Montgomery Reduction Methods

The security of sensor networks is a challenging area. Key management is one of the crucial parts in constructing the security among sensor nodes. However, key management protocols require a great deal of energy consumption, particularly in the transmission of initial key negotiation messages. In this paper, we examine three previously published sensor network security schemes: SPINS and C&R for master-key-based schemes, and Eschenhaur-Gligor (EG) for distributed-key-based schemes. We then present two new low-power schemes, which we call BROSK and OKS as alternatives to master-key-based schemes and distributed-key-based schemes, respectively. Compared to SPINS and C&R protocols, BROSK can reduce energy consumption by up to 12X by reducing the number of data transmissions in the key negotiation process. Compared with EG, OKS reduces energy by up to 96% and reduces memory requirements by up to 78%.

Reducing radio energy consumption of key management protocols for wireless sensor networks

In this paper an AES crypto coprocessor that is fabricated using a 0.18-μm CMOS technology is presented. This crypto coprocessor performs the AES-128 encryption in both feedback and non-feedback modes of operation. A maximum throughput of 3.84 Gbits/s is achieved at a 330 MHz clock frequency for ECB, OFB, and CBC modes of operation. This crypto coprocessor can be programmed using the memory-mapped interface of an embedded CPU core and is tested using a LEON 32-bit (SPARC V8) processor in the ThumbPod secure system-on-chip.

Ingrid Verbauwhede

Papers

FPGA implementation of pairings using residue number system and lazy reduction

SFINKS: A synchronous stream cipher for restricted hardware environments

Faster Interleaved Modular Multiplication Based on Barrett and Montgomery Reduction Methods

Reducing radio energy consumption of key management protocols for wireless sensor networks

A 3.84 gbits/s AES crypto coprocessor with modes of operation in a 0.18-μm CMOS technology