Ingrid Verbauwhede

Bio: Ingrid Verbauwhede is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Cryptography & Elliptic curve cryptography. The author has an hindex of 72, co-authored 575 publications receiving 21110 citations. Previous affiliations of Ingrid Verbauwhede include University of California & Massachusetts Institute of Technology.

A logic level design methodology for a secure DPA resistant ASIC or FPGA implementation

Abstract: This paper describes a novel design methodology to implement a secure DPA resistant crypto processor. The methodology is suitable for integration in a common automated standard cell ASIC or FPGA design flow. The technique combines standard building blocks to make 'new' compound standard cells, which have a close to constant power consumption. Experimental results indicate a 50 times reduction in the power consumption fluctuations.

A dynamic and differential CMOS logic with signal independent power consumption to withstand differential power analysis on smart cards

Abstract: To protect security devices such as smart cards against power attacks, we propose a dynamic and differential CMOS logic style. The logic operates with a power consumption independent of both the logic values and the sequence of the data. Consequently, it will not reveal the sensitive data in a device. We have built a set of logic gates and flip-flops needed for cryptographic functions and compared those to Static Complementary CMOS implementations.

Physically Unclonable Functions: A Study on the State of the Art and Future Research Directions

Abstract: The idea of using intrinsic random physical features to identify objects, systems, and people is not new. Fingerprint identification of humans dates at least back to the nineteenth century [21] and led to the field of biometrics. In the 1980s and 1990s of the twentieth century, random patterns in paper and optical tokens were used for unique identification of currency notes and strategic arms [2, 8, 53]. A formalization of this concept was introduced in the very beginning of the twenty-first century, first as physical one-way functions [41, 42], physical random functions [13], and finally as physical(ly) unclonable functions or PUFs.1 In the years following this introduction, an increasing number of new types of PUFs were proposed, with a tendency toward more integrated constructions. The practical relevance of PUFs for security applications was recognized from the start, with a special focus on the promising properties of physical unclonability and tamper evidence.

SPONGENT: a lightweight hash function

Abstract: This paper proposes spongent - a family of lightweight hash functions with hash sizes of 88 (for preimage resistance only), 128, 160, 224, and 256 bits based on a sponge construction instantiated with a present-type permutation, following the hermetic sponge strategy. Its smallest implementations in ASIC require 738, 1060, 1329, 1728, and 1950 GE, respectively. To our best knowledge, at all security levels attained, it is the hash function with the smallest footprint in hardware published so far, the parameter being highly technology dependent. spongent offers a lot of flexibility in terms of serialization degree and speed. We explore some of its numerous implementation trade-offs. We furthermore present a security analysis of spongent. Basing the design on a present-type primitive provides confidence in its security with respect to the most important attacks. Several dedicated attack approaches are also investigated.

I and i

Abstract: 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 …

Machine learning

Abstract: 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.).

PRESENT: An Ultra-Lightweight Block Cipher

Abstract: 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.

[서평]「Applied Cryptography」

Abstract: 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.