Practical Improvements of Profiled Side-Channel Attacks on a Hardware Crypto-Accelerator

Abstract. This article investigates the relevance of the theoretical frame-work on profiled side-channel attacks presented by F.-X. Standaert et al. at Eurocrypt 2009. The analyses consist in a case-study based on side-channel measurements acquired experimentally from a hardwired crypto-graphic accelerator. Therefore, with respect to previous formal analyses carried out on software measurements or on simulated data, the inves-tigations we describe are more complex, due to the underlying chip’s architecture and to the large amount of algorithmic noise. In this dif-ficult context, we show however that with an engineer’s mindset, two techniques can greatly improve both the off-line profiling and the on-line attack. First, we explore the appropriateness of different choices for the sensitive variables. We show that a skilled attacker aware of the regis-ter transfers occurring during the cryptographic operations can select the most adequate distinguisher, thus increasing its success rate. Sec-ond, we introduce a method based on the thresholding of leakage data to accelerate the profiling or the matching stages. Indeed, leveraging on an engineer’s common sense, it is possible to visually foresee the shape of some eigenvectors thereby anticipating their estimation towards their asymptotic value by authoritatively zeroing weak components containing mainly non-informational noise. This method empowers an attacker, in that it saves traces when converging towards correct values of the secret. Concretely, we demonstrate a 5 times speed-up in the on-line phase of the attack.

Efficient template attacks

This is the accepted manuscript version. The final published version is available from http://link.springer.com/chapter/10.1007/978-3-319-08302-5_17.Template attacks remain a powerful side-channel technique to eavesdrop on tamper-resistant hardware. They model the probability distribution of leaking signals and noise to guide a search for secret data values. In practice, several numerical obstacles can arise when implementing such attacks with multivariate normal distributions. We propose efficient methods to avoid these. We also demonstrate how to achieve significant performance improvements, both in terms of information extracted and computational cost, by pooling covariance estimates across all data values. We provide a detailed and systematic overview of many different options for implementing such attacks. Our experimental evaluation of all these methods based on measuring the supply current of a byte-load instruction executed in an unprotected 8-bit microcontroller leads to practical guidance for choosing an attack algorithm.Omar Choudary is a recipient of the Google Europe Fellowship in Mobile Security, and this research is supported in part by this Google Fellowship

Outcome of Diagnostic Tests Using Samples from Patients with Culture-Proven Human Monocytic Ehrlichiosis: Implications for Surveillance

We describe the concordance among results from various laboratory tests using samples derived from nine culture-proven cases of human monocytic ehrlichiosis (HME) caused by Ehrlichia chaffeensis. A class-specific indirect immunofluorescence assay for immunoglobulin M (IgM) and IgG, using E. chaffeensis antigen, identified 44 and 33% of the isolation-confirmed HME patients on the basis of samples obtained at initial clinical presentation, respectively; detection of morulae in blood smears was similarly insensitive (22% positive). PCR amplifications of ehrlichial DNA targeting the 16S rRNA gene, the variable-length PCR target gene, and the groESL operon were positive for whole blood specimens obtained from all patients at initial presentation. As most case definitions of HME require a serologic response with compatible illness for a categorization of even probable disease, PCR would have been required to confirm the diagnosis of HME in all nine of these patients without the submission of a convalescent-phase serum sample. These data suggest that many, if not most, cases of HME in patients who present early in the course of the disease may be missed and underscore the limitations of serologically based surveillance systems

Monitoring Motor Fluctuations in Patients With Parkinson’s Disease Using Wearable Sensors

This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson’s disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms andmotor complications.Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.Engineering and Applied Science

Improvement in hospital Quality of Care (QoC) after the introduction of rotavirus vaccination:An evaluation study in Belgium

During each winter period hospital emergency rooms and pediatric wards are often overwhelmed by high patient influx with infectious diseases leading to chaotic conditions with poor quality of care (QoC) delivery as a consequence. The conditions could be improved if we were able to better control the influx by introducing for instance better prevention strategies against some of the most frequent infectious diseases. New prevention strategies using vaccination against rotavirus infection were introduced in Belgium in November 2006. We developed a measure of hospital QoC suitable for assessing the impact of pediatric rotavirus vaccination. The study is retrospective collecting routine data on bed and staff management in one pediatric hospital in Belgium. The data were divided in pre- and post-vaccination periods during rotavirus-epidemic and non-epidemic periods. The scores were constructed using Explanatory Factor Analysis (EFA). All patients enrolled were admitted to the pediatric ward over the period from 1 January 2004 to 31 December 2009. The results of the epidemic period indicated that bed-day occupancy, bed-day turnover and unplanned readmissions for acute gastroenteritis were lower in the post-vaccination compared with the pre-vaccination periods. The QoC scores were therefore significantly lower (indicating improved QoC) after the introduction of rotavirus vaccination, compared with pre-vaccination. The data suggests that the reduction in the winter peak of rotavirus-related hospitalizations after the introduction of the vaccine reduces pressure on hospital resources and improves the quality of hospital care. The findings should be further tested in similar settings

KLEIN: A New Family of Lightweight Block Ciphers

Resource-efficient cryptographic primitives become fundamental for realizing both security and efficiency in embedded systems like RFID tags and sensor nodes. Among those primitives, lightweight block cipher plays a major role as a building block for security protocols. In this paper, we describe a new family of lightweight block ciphers named KLEIN, which is designed for resource-constrained devices such as wireless sensors and RFID tags. Compared to the related proposals, KLEIN has advantage in the software performance on legacy sensor platforms, while in the same time its hardware implementation can also be compact