PRank: Fast Analytical Rank Estimation via Pareto Distributions

Rank estimation is an important tool for a side-channel evaluations laboratories. It allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over $d$ subkeys (usually key bytes). These estimations are particularly useful where the key is not reachable with exhaustive search. We propose a new method called PRank for rank estimation, that is conceptually simple, and more time and memory efficient than previous proposals. Our main idea is to bound each subkey distribution by a Pareto-like function: since these are analytical functions, we can then estimate the rank by a closed formula. We evaluated the performance of PRank through extensive simulations based on two real SCA data corpora, and compared it to the currently-best histogram-based algorithm. We show that PRank gives a good rank estimation with much improved time and memory efficiency, especially for large ranks: For ranks between $2^{80}-2^{100}$ PRank estimation is at most 10 bits above the histogram rank and for ranks beyond $2^{100}$ the PRank estimation is only 4 bits above the histogram rank---yet it runs faster, and uses negligible memory. PRank gives a new and interesting method to solve the rank estimation problem based on reduction to analytical functions and calculating one closed formula hence using negligible time and space

A Bounded-Space Near-Optimal Key Enumeration Algorithm for Multi-Dimensional Side-Channel Attacks

Enumeration of cryptographic keys in order of likelihood based on side-channel leakages has a significant importance in cryptanalysis. Previous algorithms enumerate the keys in optimal order, however their space complexity is $\Omega(n^{d/2})$ when there are d subkeys and n candidate values per subkey. We propose a new key enumeration algorithm that has a space complexity bounded by $O(d^2 w+dn)$, when w is a design parameter, which allows the enumeration of many more keys without exceeding the available space. The trade-off is that the enumeration order is only near-optimal, with a bounded ratio between optimal and near-optimal ranks. Before presenting our algorithm we provide bounds on the guessing entropy of the full key in terms of the easy-to-compute guessing entropies of the individual subkeys. We use these results to quantify the near-optimality of our algorithm\u27s ranking, and to bound its guessing entropy. We evaluated our algorithm through extensive simulations. We show that our algorithm continues its near-optimal-order enumeration far beyond the rank at which the optimal algorithm fails due to insufficient memory, on realistic SCA scenarios. Our simulations utilize a new model of the true rank distribution, based on long tail Pareto distributions, that is validated by empirical data and may be of independent interest

Rank Estimation with Bounded Error via Exponential Sampling

Efficient rank estimation algorithms are of prime interest in security evaluation against side-channel attacks (SCA) and recently also for password strength estimators. In a side channel setting it allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over $d$ subkeys (usually key bytes). In password strength estimators the rank estimation allows estimating how many attempts a password cracker would need until it finds a given password. We propose ESrank, the first rank estimation algorithm with a bounded error ratio: its error ratio is bounded by $\gamma^{2d-2}$, for any probability distribution, where $d$ is the number of subkey dimensions and $\gamma>1$ can be chosen according to the desired accuracy. ESrank is also the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity. Our main idea is to use exponential sampling to drastically reduce the algorithm\u27s complexity. We evaluated the performance of ESrank on real SCA and password strength corpora. We show ESrank gives excellent rank estimation with roughly a 1-bit margin between lower and upper bounds in less than 1 second on the SCA corpus and 4 seconds preprocessing time and 7$\mu$sec lookup time on the password strength corpus

Real-Time Analysis of Alarm Pheromone Emission by the Pea Aphid (Acyrthosiphon Pisum) Under Predation

Upon attack by predators or parasitoids, aphids emit volatile chemical alarm signals that warn other aphids of a potential risk of predation. Release rate of the major constituent of the alarm pheromone in pea aphids (Acyrthosiphon pisum), (E)-ß-farnesene (EBF), was measured for all nymphal and the adult stage as aphids were attacked individually by lacewing (Chrysoperla carnae) larvae. Volatilization of EBF from aphids under attack was quantified continuously for 60 min at 2-min intervals with a rapid gas chromatography technique (zNose™) to monitor headspace emissions. After an initial burst, EBF volatilization declined exponentially, and detectable amounts were still present after 30 min in most cases. Total emission of EBF averaged 16.33 ± 1.54 ng and ranged from 1.18 to 48.85 ng. Emission was higher in nymphs as compared to adults. No differences between pea aphid life stages were detected for their speed of alarm signal emission in response to lacewing larvae attack. This is the first time that alarm pheromone emission from single aphids has been reported

Descriptive epidemiology of somatising tendency: findings from the CUPID study.

Somatising tendency, defined as a predisposition to worry about common somatic symptoms, is importantly associated with various aspects of health and health-related behaviour, including musculoskeletal pain and associated disability. To explore its epidemiological characteristics, and how it can be specified most efficiently, we analysed data from an international longitudinal study. A baseline questionnaire, which included questions from the Brief Symptom Inventory about seven common symptoms, was completed by 12,072 participants aged 20-59 from 46 occupational groups in 18 countries (response rate 70%). The seven symptoms were all mutually associated (odds ratios for pairwise associations 3.4 to 9.3), and each contributed to a measure of somatising tendency that exhibited an exposure-response relationship both with multi-site pain (prevalence rate ratios up to six), and also with sickness absence for non-musculoskeletal reasons. In most participants, the level of somatising tendency was little changed when reassessed after a mean interval of 14 months (75% having a change of 0 or 1 in their symptom count), although the specific symptoms reported at follow-up often differed from those at baseline. Somatising tendency was more common in women than men, especially at older ages, and varied markedly across the 46 occupational groups studied, with higher rates in South and Central America. It was weakly associated with smoking, but not with level of education. Our study supports the use of questions from the Brief Symptom Inventory as a method for measuring somatising tendency, and suggests that in adults of working age, it is a fairly stable trait

The burden of antimicrobial resistance in the Americas in 2019: a cross-country systematic analysis

Background Antimicrobial resistance (AMR) is an urgent global health challenge and a critical threat to modern health care. Quantifying its burden in the WHO Region of the Americas has been elusive—despite the region’s long history of resistance surveillance. This study provides comprehensive estimates of AMR burden in the Americas to assess this growing health threat. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with AMR for 23 bacterial pathogens and 88 pathogen–drug combinations for countries in the WHO Region of the Americas in 2019. We obtained data from mortality registries, surveillance systems, hospital systems, systematic literature reviews, and other sources, and applied predictive statistical modelling to produce estimates of AMR burden for all countries in the Americas. Five broad components were the backbone of our approach: the number of deaths where infection had a role, the proportion of infectious deaths attributable to a given infectious syndrome, the proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of pathogens resistant to an antibiotic class, and the excess risk of mortality (or duration of an infection) associated with this resistance. We then used these components to estimate the disease burden by applying two counterfactual scenarios: deaths attributable to AMR (compared to an alternative scenario where resistant infections are replaced with susceptible ones), and deaths associated with AMR (compared to an alternative scenario where resistant infections would not occur at all). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. Findings We estimated 569,000 deaths (95% UI 406,000–771,000) associated with bacterial AMR and 141,000 deaths (99,900–196,000) attributable to bacterial AMR among the 35 countries in the WHO Region of the Americas in 2019. Lower respiratory and thorax infections, as a syndrome, were responsible for the largest fatal burden of AMR in the region, with 189,000 deaths (149,000–241,000) associated with resistance, followed by bloodstream infections (169,000 deaths [94,200–278,000]) and peritoneal/intra-abdominal infections (118,000 deaths [78,600–168,000]). The six leading pathogens (by order of number of deaths associated with resistance) were Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Streptococcus pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. Together, these pathogens were responsible for 452,000 deaths (326,000–608,000) associated with AMR. Methicillin-resistant S. aureus predominated as the leading pathogen–drug combination in 34 countries for deaths attributable to AMR, while aminopenicillin-resistant E. coli was the leading pathogen–drug combination in 15 countries for deaths associated with AMR. Interpretation Given the burden across different countries, infectious syndromes, and pathogen–drug combinations, AMR represents a substantial health threat in the Americas. Countries with low access to antibiotics and basic health-care services often face the largest age-standardised mortality rates associated with and attributable to AMR in the region, implicating specific policy interventions. Evidence from this study can guide mitigation efforts that are tailored to the needs of each country in the region while informing decisions regarding funding and resource allocation. Multisectoral and joint cooperative efforts among countries will be a key to success in tackling AMR in the Americas.publishedVersio

Public health utility of cause of death data : applying empirical algorithms to improve data quality

Background: Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments. Methods: We describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings. Results: The proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7% of all vital registration deaths to 67.3% in 2015, and the age-standardized proportion had an overall negative association with the Socio Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30% of garbage-coded deaths in those under 1 year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD