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

A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2008 Springer Science + Business Media LLC

A New U.S.-U.S.S.R. Seismological Program

On July 9, 1986, a team of researchers from the University of California, San Diego; University of Nevada, Reno; and the University of Colorado, Boulder established the first of three seismic stations to be located in the vicinity of the Soviet nuclear test site in eastern Kazakhstan (KTS) (see cover). Under an agreement reached between the Soviet Academy of Sciences and the Natural Resources Defense Council, a nonprofit U.S. environmental organization, these stations, which are configured to meet the specifications of the proposed new global seismographic network [Incorporated Research Institutions for Seismology (IRIS), 1984], will be complemented by three similarly equipped stations to be installed in the vicinity of the U.S. nuclear test site in southern Nevada (NTS). The stations are to be operated cooperatively by Soviet and U.S. personnel (Figure 1)

Genetic polymorphisms in DNA repair and damage response genes and late normal tissue complications of radiotherapy for breast cancer

Breast-conserving surgery followed by radiotherapy is effective in reducing recurrence; however, telangiectasia and fibrosis can occur as late skin side effects. As radiotherapy acts through producing DNA damage, we investigated whether genetic variation in DNA repair and damage response confers increased susceptibility to develop late normal skin complications. Breast cancer patients who received radiotherapy after breast-conserving surgery were examined for late complications of radiotherapy after a median follow-up time of 51 months. Polymorphisms in genes involved in DNA repair (APEX1, XRCC1, XRCC2, XRCC3, XPD) and damage response (TP53, P21) were determined. Associations between telangiectasia and genotypes were assessed among 409 patients, using multivariate logistic regression. A total of 131 patients presented with telangiectasia and 28 patients with fibrosis. Patients with variant TP53 genotypes either for the Arg72Pro or the PIN3 polymorphism were at increased risk of telangiectasia. The odds ratios (OR) were 1.66 (95% confidence interval (CI): 1.02–2.72) for 72Pro carriers and 1.95 (95% CI: 1.13–3.35) for PIN3 A2 allele carriers compared with non-carriers. The TP53 haplotype containing both variant alleles was associated with almost a two-fold increase in risk (OR 1.97, 95% CI: 1.11–3.52) for telangiectasia. Variants in the TP53 gene may therefore modify the risk of late skin toxicity after radiotherapy

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration