ÐThis 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.
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.
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, c 1-(c 2-(c 3-(c 4-c 5)))) 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 first t trials.
The algorithm for pac learning k-DNF or k-CNF in the presence of malicious attribute noise in polynomial time claimed by Sloan [S1088] does not work.It is currently open whether such an algorithm exists.
We introduce an unusual approach for implementing cryptographic arithmetic in short high-level code with machinechecked proofs of functional correctness. We further demonstrate that simple partial evaluation is sufficient to transform such initial code into highly competitive C code, breaking the decades-old pattern that the only fast implementations are those whose instruction-level steps were written out by hand.These techniques were used to build an elliptic-curve library that achieves competitive performance for a wide range of prime fields and multiple CPU architectures, showing that implementation and proof effort scales with the number and complexity of conceptually different algorithms, not their use cases. As one outcome, we present the first verified highperformance implementation of P-256, the most widely used elliptic curve. Implementations from our library were included in BoringSSL to replace existing specialized code, for inclusion in several large deployments for Chrome, Android, and CloudFlare. This is an abridged version of the full paper originally presented in IEEE S&P 2019 [10]. We have omitted most proof-engineering details in favor of a focus on the system's functional capabilities.
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