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」

Finite Field Arithmetic

Implementing large word-length public keyalgorithms onsmall 8-bit it-controllers isachallenge. Thispaperpresents a hardware/software co-design solution ofRSA andElliptic Curve Cryptography (ECC)overGF(p) ona 12MHz 8-bit 8051it-controller. Thehardware coprocessor hasamodular arithmetic logic unit (MALU)ofwhichthedigit size (d)isvariable. It canbeadapted tothespeed andbandwidth oftheit-controller towhichitisconnected. The HW/SWco-design space exploration isbased ontheGEZELsystem-level design environment. It allows thedesigner tofind thebest performance-area combination forthedigit size. A casestudy ofanFPGAimplementation fora160-bit ECCoverGF(p)(ECC-160p) showsthat onepoint multiplication canbecomputed 40times faster thananoptimized SW implementation withthe optimized digit size, d=4.

HW/SW Co-design forAccelerating Public-Key Cryptosystems overGF(p)onthe8051j-controller

Micro-architectural side-channel leakage received a lot of attention due to their high impact on software security on complex out-of-order processors. These are extremely specialised threat models and can be only realised in practise with high precision measurement code, triggering micro-architectural behavior that leaks information. In this paper, we present a tool to support the inexperienced user to verify his code for side-channel leakage. We combine two very useful tools- statistical testing and hardware performance monitors to bridge this gap between the understanding of the general purpose users and the most precise speculative execution attacks. We first show that these event counters are more powerful than observing timing variabilities on an executable. We extend Dudect, where the raw hardware events are collected over the target executable, and leakage detection tests are incorporated on the statistics of observed events following the principles of non-specific t-tests. Finally, we show the applicability of our tool on the most popular speculative micro-architectural and data-sampling attack models.

Exploring Micro-architectural Side-Channel Leakages through Statistical Testing

Post-processing of the raw bits produced by a true random number generator (TRNG) is always necessary when the entropy per bit is insufficient for security applications. In this paper, we derive a tight bound on the output min-entropy of the algorithmic post-processing module based on linear codes, known as linear correctors. Our bound is based on the codes' weight distributions, and we prove that it holds even for the real-world noise sources that produce independent but not identically distributed bits. Additionally, we present a method for identifying the optimal linear corrector for a given input min-entropy rate that maximizes the throughput of the post-processed bits while simultaneously achieving the needed security level. Our findings show that for an output min-entropy rate of $0.999$, the extraction efficiency of the linear correctors with the new bound can be up to $130.56\%$ higher when compared to the old bound, with an average improvement of $41.2\%$ over the entire input min-entropy range. On the other hand, the required min-entropy of the raw bits for the individual correctors can be reduced by up to $61.62\%$.

Optimizing Linear Correctors: A Tight Output Min-Entropy Bound and Selection Technique

This paper describes the architecture and the performance of a new programmable 16-bit digital signal processor (DSP) engine It is developed specifically for next generation wireless digital systems and speech applications Besides providing a basic instruction set, similar to current day 16-bit DSPs, it contains extra architectural features and unique instructions, which make the engine highly efficient for compute-intensive tasks such as vector quantization and Viterbi operations The datapath contains two multiply-accumulate units and one ALU The external memory bandwidth is kept to two data buses and two corresponding address buses Still, the internal bus network is designed such that all three units are operating in parallel This parallelism is reflected in the performance benchmarks For example, an FIR filter of N taps will take N/2 instruction cycles compared to N for a general purpose 16-bit DSP, and it requires only half the number of memory accesses of a general purpose DSP

http://ieeexplore.ieee.org/iel3/4071/12058/00558380.pdf

Ingrid Verbauwhede

Papers

Finite Field Arithmetic

HW/SW Co-design forAccelerating Public-Key Cryptosystems overGF(p)onthe8051j-controller

Exploring Micro-architectural Side-Channel Leakages through Statistical Testing

Optimizing Linear Correctors: A Tight Output Min-Entropy Bound and Selection Technique

A low power DSP engine for wireless communications