J Adolesc Health

PurposeThe goal of the present research was to identify distinct latent classes of adolescents that commit teen dating violence (TDV) and assess differences on demographic, behavioral, and attitudinal correlates.MethodsBoys and girls (N = 1,149; Mage = 14.3; Grades 6\u201312) with a history of violence exposure completed surveys assessing six indices of TDV in the preceding 3 months. Indices of TDV included controlling behaviors, psychological TDV, physical TDV, sexual TDV, fear/intimidation, and injury. In addition, adolescents provided demographic and dating history information and completed surveys assessing attitudes condoning violence, relationship skills and knowledge, and reactive/proactive aggression.ResultsLatent class analysis indicated a three-class solution wherein the largest class of students was nonviolent on all indices (\u201cnonaggressors\u201d) and the smallest class of students demonstrated high probability of nearly all indices of TDV (\u201cmultiform aggressors\u201d). In addition, a third class of \u201cemotional aggressors\u201d existed for which there was a high probability of controlling and psychological TDV but low likelihood of any other form of TDV. Multiform aggressors were differentiated from emotional and nonaggressors on the use of self-defense in dating relationships, attitudes condoning violence, and proactive aggression. Emotional aggressors were distinguished from nonaggressors on nearly all measured covariates.ConclusionsEvidence indicates that different subgroups of adolescents engaging in TDV exist. In particular, a small group of youth engaging in multiple forms of TDV can be distinguished from a larger group of youth that commit acts of TDV restricted to emotional aggression (i.e., controlling and psychological) and most youth that do not engage in TDV.CC999999/Intramural CDC HHS/United States2018-03-20T00:00:00Z26683984PMC5860637vault:2759

The Side-Channel Metrics Cheat Sheet

Side-channel attacks exploit a physical observable originating from a cryptographic device in order to extract its secrets. Many practically relevant advances in the field of side-channel analysis relate to security evaluations of cryptographic functions and devices. Accordingly, many metrics have been adopted or defined to express and quantify side-channel security. These metrics can relate to one another, but also conflict in terms of effectiveness, assumptions and security goals. In this work, we review the most commonly used metrics in the field of side-channel analysis. We provide a self-contained presentation of each metric, along with a discussion of its limitations. We practically demonstrate the metrics on examples of relevant implementations of the Advanced Encryption Standard (AES), and make the software implementation of the presented metrics available to the community as open source. This work, being beyond a survey of the current status of metrics, will allow researchers and practitioners to produce a well-informed security evaluation through a better understanding of its supporting and summarizing metrics

How the global structure of protein interaction networks evolves

Two processes can influence the evolution of protein interaction networks: addition and elimination of interactions between proteins, and gene duplications increasing the number of proteins and interactions. The rates of these processes can be estimated from available Saccharomyces cerevisiae genome data and are sufficiently high to affect network structure on short time scales. For instance, more than 100 interactions may be added to the yeast network every million years, a substantial fraction of which adds previously unconnected proteins to the network. Highly connected proteins show a greater rate of interaction turnover than proteins with few interactions. From these observations one can explain ? without natural selection on global network structure ? the evolutionary sustenance of the most prominent network feature, the distribution of the frequency P(d) of proteins with d neighbors, which is a broad-tailed distribution. This distribution is independent of the experimental approach providing nformation on network structure

MCRank: Monte Carlo Key Rank Estimation for Side-Channel Security Evaluations

Key rank estimation provides a measure of the effort that the attacker has to spend bruteforcing the key of a cryptographic algorithm, after having gained some information from a side channel attack. We present MCRank, a novel method for key rank estimation based on Monte Carlo sampling. MCRank provides an unbiased estimate of the rank and a confidence interval. Its bounds rapidly become tight for increasing sample size, with a corresponding linear increase of the execution time. When applied to evaluate an AES-128 implementation, MCRank can be orders of magnitude faster than the state-of-the-art histogram-based enumeration method for comparable bound tightness. It also scales better than previous work for large keys, up to 2048 bytes. Besides its conceptual simplicity and efficiency, MCRank can assess for the first time the security of large keys even if the probability distributions given the side channel leakage are not independent between subkeys, which occurs, for example, when evaluating the leakage security of an AES-256 implementation

GE vs GM: Efficient side-channel security evaluations on full cryptographic keys

Security evaluations for full cryptographic keys is a very important research topic since the past decade. An efficient rank estimation algorithm was proposed at FSE 2015 to approximate the empirical guessing entropy remaining after a side-channel attack on a full AES key, by combining information from attacks on each byte of he key independently. However, these could not easily scale to very large keys over 1024 bits. Hence, at CHES 2017, it was proposed a new approach for scalable security evaluations based on Massey’s guessing entropy, which was shown tight and scalable to very large keys, even beyond 8192 bits. Then, at CHES 2020, it was proposed a new method for estimating the empirical guessing entropy for the case of full-key evaluations, showing also important divergences between the empirical guessing entropy and Massey’s guessing entropy. However, there has been some confusion in recent publications of side-channel evaluation methods relying on these two variants of the guessing entropy. Furthermore, it remained an open problem to decide which of these methods should be used and in which context, particularly given the wide acceptance of the empirical guessing entropy in the side-channel community and the relatively little use of the other. In this paper, we tackle this open problem through several contributions. First of all, we provide an unitary presentation of both versions of the guessing entropy, allowing an easy comparison of the two metrics. Secondly, we compare the two metrics using a set of common and relevant indicators, as well as three different datasets for side-channel evaluations (simulated, AVR XMEGA 8-bit microcontroller and a 32-bit device). We used these indicators and datasets also to compare the three full-key evaluation methods from FSE 2015, CHES 2017 and CHES 2020, allowing us to provide a clear overview of the usefulness and limitations of each method. Furthermore, our analysis has enabled us to find a new method for verifying the soundness of a leakage model, by comparing both versions of the guessing entropy. This method can be easily extended to full-key evaluations, hence leading to a new useful method for side-channel evaluations

Back to Massey: Impressively fast, scalable and tight security evaluation tools

None of the existing rank estimation algorithms can scale to large cryptographic keys, such as 4096-bit (512 bytes) RSA keys. In this paper, we present the first solution to estimate the guessing entropy of arbitrarily large keys, based on mathematical bounds, resulting in the fastest and most scalable security evaluation tool to date. Our bounds can be computed within a fraction of a second, with no memory overhead, and provide a margin of only a few bits for a full 128-bit AES key

Understanding the effect size and its measures.

The evidence based medicine paradigm demands scientific reliability, but modern research seems to overlook it sometimes. The power analysis represents a way to show the meaningfulness of findings, regardless to the emphasized aspect of statistical significance. Within this statistical framework, the estimation of the effect size represents a means to show the relevance of the evidences produced through research. In this regard, this paper presents and discusses the main procedures to estimate the size of an effect with respect to the specific statistical test used for hypothesis testing. Thus, this work can be seen as an introduction and a guide for the reader interested in the use of effect size estimation for its scientific endeavour

Statistical modelling of key variables in social survey data analysis

The application of statistical modelling techniques has become a cornerstone of analyses of large-scale social survey data. Bringing this special section on key variables to a close, this final article discusses several important issues relating to the inclusion of key variables in statistical modelling analyses. We outline two, often neglected, issues that are relevant to a great many applications of statistical models based upon social survey data. The first is known as the reference category problem and is related to the interpretation of categorical explanatory variables. The second is the interpretation and comparison of the effects from models for non-linear outcomes. We then briefly discuss other common complexities in using statistical models for social science research; these include the non-linear transformation of variables, and considerations of intersectionality and interaction effects. We conclude by emphasising the importance of two, often overlooked, elements of the social survey data analysis process, sensitivity analysis and documentation for replication. We argue that more attention should routinely be devoted to these issues

Prevalence of and factors associated with non-partner rape perpetration: fi ndings from the UN Multi-country Crosssectional Study on Men and Violence in Asia and the Pacifi c

Background Rape perpetration is under-researched. In this study, we aimed to describe the prevalence of, and factors associated with, male perpetration of rape of non-partner women and of men, and the reasons for rape, from nine sites in Asia and the Pacifi c across six countries: Bangladesh, China, Cambodia, Indonesia, Papua New Guinea, and Sri Lanka. Methods In this cross-sectional study, undertaken in January 2011–December 2012, for each site we chose a multistage representative sample of households and interviewed one man aged 18–49 years from each. Men self-completed questions about rape perpetration. We present multinomial regression models of factors associated with single and multiple perpetrator rape and multivariable logistic regression models of factors associated with perpetration of male rape with population-attributable fractions. Findings We interviewed 10 178 men in our study (815–1812 per site). The prevalence of non-partner single perpetrator rape varied between 2·5% (28/1131; rural Bangladesh) and 26·6% (225/846; Bougainville, Papua New Guinea), multiple perpetrator rape between 1·4% (18/1246; urban Bangladesh) and 14·1% (119/846; Bougainville, Papua New Guinea), and male rape between 1·5% (13/880; Jayapura, Indonesia) and 7·7% (65/850; Bougainville, Papua New Guinea). 57·5% (587/1022) of men who raped a non-partner committed their fi rst rape as teenagers. Frequent reasons for rape were sexual entitlement (666/909; 73·3%, 95% CI 70·3–76·0), seeking of entertainment (541/921; 58·7%, 55·0–62·4), and as a punishment (343/905; 37·9%, 34·5–41·4). Alcohol was a factor in 249 of 921 cases (27·0%, 95% CI 24·2–30·1). Associated factors included poverty, personal history of victimisation (especially in childhood), low empathy, alcohol misuse, masculinities emphasising heterosexual performance, dominance over women, and participation in gangs and related activities. Only 443 of 1933 men (22·9%, 95% CI 20·7–25·3) who had committed rape had ever been sent to prison for any period. Interpretation Rape perpetration committed by men is quite frequent in the general population in the countries studied, as it is in other countries where similar research has been undertaken, such as South Africa. Prevention of rape is essential, and interventions must focus on childhood and adolescence, and address culturally rooted male gender socialisation and power relations, abuse in childhood, and poverty