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」

Smart medical devices will only be smart if they also include technology to provide security and privacy. In practice this means the inclusion of cryptographic algorithms of sufficient cryptographic strength. For battery operated devices or for passively powered devices, these cryptographic algorithms need highly efficient, low power, low energy realizations. Moreover, unique to cryptographic implementations is that they also need protection against physical tampering either active or passive. This means that countermeasures need to be included during the design process. Similar to design for low energy, design for physical protection needs to be addressed at all design abstraction levels. Differently, while skipping one optimization step in a design for low energy or low power, merely reduces the battery life time, skipping a countermeasure, means opening the door for a possible attack. Designing for security requires a thorough threat analysis and a balanced selection of countermeasures. This paper will discuss the different abstraction layers and design methods applied to obtain low power/low energy and at the same time side-channel and fault attack resistant cryptographic implementations. To provide a variety of security features, including location privacy, it is clear that medical devices need public key cryptography (PKC). It will be illustrated with the design of a low energy elliptic curve based public key programmable co-processor. It only needs 5.1μJ of energy in a 0.13 μm CMOS technology for one point multiplication and includes a selected set of countermeasures against physical attacks.

/pdf/low-energy-encryption-for-medical-devices-security-adds-an-124vmdpr62.pdf

Low-energy encryption for medical devices: security adds an extra design dimension

https://lirias.kuleuven.be/bitstream/123456789/228647/2/article-1183.pdf

Partition vs. Comparison Side-Channel Distinguishers

Implementing cryptography and security into integrated circuits is somehow similar to applications in other fields. We have to worry about comparable optimization goals: area, power, energy, throughput and/or latency. Moore's law helps to attain these goals. However, it also gives the attackers more computational power to break cryptographic algorithms. On top of this, quantum computers may become soon a reality, so that novel, very computationally demanding "post-quantum" cryptographic algorithms need implementation. Finally, there is a third dimension to the problem: implementations have to be resistant against physical attacks and countermeasures increase the cost. This paper demonstrates with actual data how these conflicting challenges are being addressed.

24.1 Circuit challenges from cryptography

History has shown that attacks against network-connected software based systems are common and dangerous. An important fraction of these attacks exploit implementation details of the software based system. These attacks - sometimes called low-level attacks - rely on characteristics of the hardware, compiler or operating system used to execute software programs to make these programs misbehave, or to extract sensitive information from them. With the increased Internet-connectivity of embedded devices, including industrial control systems, sensors as well as consumer devices, there is a substantial risk that similar attacks will target these devices. This tutorial paper explains the vulnerabilities, attacks and countermeasures relevant for low-level software security. The paper discusses software security for two different attacker models: the classic model of an attacker that can only interact with the program by providing input and reading output, and the more recent and challenging model of an attacker that controls part of the execution platform on which the software runs, for instance because the attacker has compromised the operating system, or some of the libraries that the software under attack relies on.

/pdf/software-security-vulnerabilities-and-countermeasures-for-vrxx8jqrnm.pdf

Software security: Vulnerabilities and countermeasures for two attacker models

Turbo codes are introduced in 3rd generation wireless cellular standards for their superior coding gain. The MIPS requirements of turbo codes are however extremely high. This paper describes the implementation of a turbo-decoding algorithm on the TMS320C55x processor. The coding performance is evaluated with fixed-point arithmetic. A method to optimize the memory is also introduced to address the large data storage problem. The effect of finite word lengths is carefully examined to reduce the state metric normalization time and to achieve at the same time an acceptable bit error rate (BER). The coding gain of 5.8 dB for a BER of 10/sup -3/ in 6 iterations is achieved with a frame size of 1 K bits and 50 Hz frame rate. Total MIPS estimate when using the max Log-MAP algorithm is 46 MIPS.

Ingrid Verbauwhede

Papers

Low-energy encryption for medical devices: security adds an extra design dimension

Partition vs. Comparison Side-Channel Distinguishers

24.1 Circuit challenges from cryptography

Software security: Vulnerabilities and countermeasures for two attacker models

Turbo codes on the fixed point DSP TMS320C55x