Robert H. Sloan

Examining smart-card security under the threat of power analysis attacks

This paper examines how monitoring power consumption signals might breach smart-card security. Both simple power analysis and differential power analysis attacks are investigated. The theory behind these attacks is reviewed. Then, we concentrate on showing how power analysis theory can be applied to attack an actual smart card. We examine the noise characteristics of the power signals and develop an approach to model the signal-to-noise ratio (SNR). We show how this SNR can be significantly improved using a multiple-bit attack. Experimental results against a smart-card implementation of the Data Encryption Standard demonstrate the effectiveness of our multiple-bit attack. Potential countermeasures to these attacks are also discussed.

Power Analysis Attacks of Modular Exponentiation in Smartcards

Three new types of power analysis attacks against smartcard implementations of modular exponentiation algorithms are described. The first attack requires an adversary to exponentiate many random messages with a known and a secret exponent. The second attack assumes that the adversary can make the smartcard exponentiate using exponents of his own choosing. The last attack assumes the adversary knows the modulus and the exponentiation algorithm being used in the hardware. Experiments show that these attacks are successful. Potential countermeasures are suggested.

Computing Curricula 2005: The Overview Report

In 2001, the ACM and the IEEE-CS published Computing Curricula 2001 which contains curriculum recommendations for undergraduate programs in computer science. That report also called for additional discipline-specific volumes for each of computer engineering, information systems, and software engineering. In addition, it called for an Overview Volume to provide a synthesis of the various volumes. The Computing Curricula 2004 Task Force undertook the job of fulfilling the latter charge. The purpose of this session is to present the recently completed work of that Task Force, now known as Computing Curricula 2005 (CC2005), and to generate discussion among, and feedback from SIGCSE members about ongoing and future work.

Learning nested differences of intersection-closed concept classes

This paper introduces a new framework for constructing learning algorithms. Our methods involve master algorithms which use learning algorithms for intersection-closed concept classes as subroutines. For example, we give a master algorithm capable of learning any concept class whose members can be expressed as nested differences (for example, c1 − (c2 − (c3 − (c4 − c5)))) of concepts from an intersection-closed class. We show that our algorithms are optimal or nearly optimal with respect to several different criteria. These criteria include: the number of examples needed to produce a good hypothesis with high confidence, the worst case total number of mistakes made, and the expected number of mistakes made in the firstt trials.

Learning integer lattices

The problem of learning an integer lattice of

Reduction rules for time Petri nets

{\bf Z}^k

Divergence caching in client-server architectures

in an on-line fashion is considered. That is, the learning algorithm is given a sequence of k-tuples of integers and predicts for each tuple in the sequence whether it lies in a hidden target lattice of

Can PAC learning algorithms tolerate random attribute noise

{\bf Z}^k

Learning Nested Differences of Intersection-Closed Concept Classes

. The goal of the algorithm is to minimize the number of prediction mistakes. An efficient learning algorithm with an absolute mistake bound of

CS 0.5: a better approach to introductory computer science for majors

k + \lfloor k\log (n\sqrt k ) \rfloor