Abstract
Since leakage detection was introduced as a popular side-channel security assessment, it has been plagued by false-positives (a.k.a. type I errors). To fix this error, the previous solutions set detection thresholds based on an assumption-based prediction of false-positive rate (FPR). However, this study points out that such a prediction (of FPR) may be inaccurate. We notice that the prediction in EuroCrypt2016 is much smaller than (approximately 1 / 779 times) the true FPR. The gap between prediction and truth, called underpredicted false-positives (UFP), leads to severe false-positives in leakage detection. Then, we check the statistical distribution of test statistics to analyze the cause of UFP. Our analysis indicates that the overlap between cross-validation (CV) blocks gives rise to an assumption error in the distribution of the CV-based estimates of ρ -statistics, which is the root cause of UFP. Therefore, we tackle the UFP by eliminating the overlap between blocks. Specifically, we propose a profiling-shared validation (PSV) and utilize this validation to improve the detection of any-variate any-order leakages. Our experiments show that the PSV solves the UFP and saves more than 75% of the test time costs. In summary, this article reports a potential flaw in leakage detection and provides a complete analysis of the flaw for the first time.
Highlights
Side-channel attack (SCA) utilizes the physical leakages of a running device to retrieve some secrets inside the device. Since it was proposed by Kocher [1], such an attack has seriously threatened the security of cryptographic modules, including smart cards [2, 3] and FPGAs [4, 5]. us, side-channel security assessments have been developed to evaluate the security of these modules against SCA [6]
In EuroCrypt2016, Durvaux et al found that test vector leakage assessment (TVLA) failed to detect the plaintext-dependent leakages [10]. en, they put forward a correlation-based leakage detection to identify the hard-to-detect leakages for TVLA. e ρCV-test takes advantage of cross-validation (CV) to obtain a wellestimated ρ-statistic rz,CV and compares the rz,CV with a threshold of 5.0 to assess any-variate any-order leakages
Due to the overlap between the training and the test blocks, there is a nonnegligible error between the assumed distribution and the true distribution. is error explains well why the predicted FPR (PFPR) of the ρCV-test deviates from the truth. ird, we present a new time-efficient validation, named profilingshared validation (PSV) to tackle the underpredicted false-positives (UFP). e profiling-shared validation (PSV) splits the samples into m + k nonoverlapping subsets and assigns these subsets to different blocks—m subsets are allocated to the training block, and the other k subsets are mutually exclusive to k test blocks. e experiments show that our PSV solves the UFP and reduces the time cost by more than 75%
Summary
Side-channel attack (SCA) utilizes the physical leakages (such as execution time [1], power consumption [2], and electromagnetic radiation [3]) of a running device to retrieve some secrets (e.g., the private key) inside the device. In contrast to the attack-based assessment [6], leakage detection exploits the dependency between leakage and data rather than the key recovery. Is assessment utilizes Weltch’s t-test to compare t-statistic with a threshold of 4.5 and identifies the leakages dependent on the plaintext. The frequently used solutions carefully set the threshold for leakage detection according to an acceptable false-positive rate (FPR) [16, 17] Such solutions rely on an assumed distribution of the test statistics to make a fast prediction of FPR. We notice the underpredicted false-positives (UFP) that the true FPR (of ρCV-test) is about 779 times the prediction (in [10] at the threshold of 5.0).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
