On Error Detection and Recovery in Elliptic Curve Cryptosystems

Abstract

Fault analysis attacks represent a serious threat to a wide range of cryptosystems including those based on elliptic curves. With the variety and demonstrated practicality of these attacks, it is essential for cryptographic implementations to handle different types of errors properly and securely. In this work, we address some aspects of error detection and recovery in elliptic curve cryptosystems. In particular, we discuss the problem of wasteful computations performed between the occurrence of an error and its detection and propose solutions based on frequent validation to reduce that waste. We begin by presenting ways to select the validation frequency in order to minimize various performance criteria including the average and worst-case costs and the reliability threshold. We also provide solutions to reduce the sensitivity of the validation frequency to variations in the statistical error model and its parameters. Then, we present and discuss adaptive error recovery and illustrate its advantages in terms of low sensitivity to the error model and reduced variance of the resulting overhead especially in the presence of burst errors. Moreover, we use statistical inference to evaluate and fine-tune the selection of the adaptive policy. We also address the issue of validation testing cost and present a collection of coherency-based, cost-effective tests. We evaluate variations of these tests in terms of cost and error detection effectiveness and provide infective and reduced-cost, repeated-validation variants. Moreover, we use coherency-based tests to construct a combined-curve countermeasure that avoids the weaknesses of earlier related proposals and provides a flexible trade-off between cost and effectiveness.