Life stressors, anger and internalization, and substance abuse among American Indian adolescents in the Midwest: an empirical test of general strain theory

Agnew\u27s general strain theory (1985, 1989, 1992) has been tested several times since its development in the last decade. This theory, however, has seldom been applied to minority groups, such as American Indian population. Using a sample of 212 American Indian 5th to 8th grade adolescents, this analysis tests general strain theory by tracing the linkage among the measures of perceived discrimination, negative life events, family conflict, anger and internalization, and early onset of substance abuse. Mediating effects of anger and internalization were investigated using structural equation models. In addition, the strength of the stressor-substance abuse relationship was examined across groups with different levels of personal/social resources. High prevalence of substance abuse and life stressors, such as negative life events and perceived discrimination were found among these American Indian adolescents. Multiple indicators of life stressors were found to have positive effects on early onset of substance abuse directly and indirectly through self-reported anger. Specifically, effects of inconsistent parenting on adolescents\u27 substance abuse were completely mediated through reports of anger. Negative life events directly affected substance abuse and had indirect effects on substance abuse through anger. Perceived discrimination led to negative affects such as internalization symptoms, but did not have significant effects on substance abuse. This study confirmed the mediating role of anger linking stressors and substance abuse; however, no mediating role of internalization was found. Furthermore, there was evidence that the strength of the anger-substance abuse relationship varied across groups with different levels of social/personal resources. With increasing levels of anger, adolescents with high self-esteem, negative attitudes toward deviance, and low levels of association with deviant peers were less likely to engage in substance abuse, compared with those with low level of self-esteem, positive attitudes toward deviance, and high levels of association with deviant peers. The relationship of life stressors and negative emotion (anger and internalization) was not moderated by social and personal resource variables. This study provided strong support to general strain theory and broadened its empirical generality to American Indian adolescents

Relational interaction and embodiment: conceptualizing meanings of LGBTQ+ activism in digital China

This article theoretically and empirically explores meanings of recent activism practised by lesbian, gay, bisexual, transgender, queer and other non-heterosexual groups (LGBTQ+) in China. Chinese LGBTQ+ individuals, like the majority of Chinese citizens, are generally self-restrained in popular contention because of the political risks involved. They also face widespread discrimination from the public when revealing their LGBTQ+ identities. This article is concerned with the perceived meanings of Chinese LGBTQ+ individuals suppressing engrained self-constraint to promote LGBTQ+ contention and certain level of collective action. Theoretically, I conceptualize Chinese LGBTQ+ protests as relational interactions undertaken by LGBTQ+ individuals with other people of queer identities (ingroup members), authorities and the public based on the logic of connective action. I also explore the concepts of embodiment and online embodiment to understand individuals’ sensual experiences during LGBTQ+ contention. Empirically, I examine university student Qiu Bai’s lawsuits with the Education Ministry and her social media campaign against homophobic textbooks. Drawing on in-depth interviews and textual analysis, the case study provides a dialectical account of individuals’ experience of embodiment and self-constraint

Optimal Attack against Autoregressive Models by Manipulating the Environment

We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). In this threat model, the environment evolves according to a dynamical system; an autoregressive model observes the current environment state and predicts its future values; an attacker has the ability to modify the environment state in order to manipulate future autoregressive forecasts. The attacker's goal is to force autoregressive forecasts into tracking a target trajectory while minimizing its attack expenditure. In the white-box setting where the attacker knows the environment and forecast models, we present the optimal attack using LQR for linear models, and Model Predictive Control (MPC) for nonlinear models. In the black-box setting, we combine system identification and MPC. Experiments demonstrate the effectiveness of our attacks

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion

Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the requirements in preserving \textit{privacy} and maintaining high model \textit{utility}. The nature of the widely-adopted protection mechanisms including \textit{Randomization Mechanism} and \textit{Compression Mechanism} is to protect privacy via distorting model parameter. We measure the utility via the gap between the original model parameter and the distorted model parameter. We want to identify under what general conditions privacy-preserving federated learning can achieve near-optimal utility via data generation and parameter distortion. To provide an avenue for achieving near-optimal utility, we present an upper bound for utility loss, which is measured using two main terms called variance-reduction and model parameter discrepancy separately. Our analysis inspires the design of appropriate protection parameters for the protection mechanisms to achieve near-optimal utility and meet the privacy requirements simultaneously. The main techniques for the protection mechanism include parameter distortion and data generation, which are generic and can be applied extensively. Furthermore, we provide an upper bound for the trade-off between privacy and utility, which together with the lower bound illustrated in NFL form the conditions for achieving optimal trade-off

Deciphering the Interplay between Local Differential Privacy, Average Bayesian Privacy, and Maximum Bayesian Privacy

The swift evolution of machine learning has led to emergence of various definitions of privacy due to the threats it poses to privacy, including the concept of local differential privacy (LDP). Although widely embraced and utilized across numerous domains, this conventional approach to measure privacy still exhibits certain limitations, spanning from failure to prevent inferential disclosure to lack of consideration for the adversary's background knowledge. In this comprehensive study, we introduce Bayesian privacy and delve into the intricate relationship between LDP and its Bayesian counterparts, unveiling novel insights into utility-privacy trade-offs. We introduce a framework that encapsulates both attack and defense strategies, highlighting their interplay and effectiveness. The relationship between LDP and Maximum Bayesian Privacy (MBP) is first revealed, demonstrating that under uniform prior distribution, a mechanism satisfying $\xi$-LDP will satisfy $\xi$-MBP and conversely $\xi$-MBP also confers 2$\xi$-LDP. Our next theoretical contribution are anchored in the rigorous definitions and relationships between Average Bayesian Privacy (ABP) and Maximum Bayesian Privacy (MBP), encapsulated by equations $\epsilon_{p,a} \leq \frac{1}{\sqrt{2}}\sqrt{(\epsilon_{p,m} + \epsilon)\cdot(e^{\epsilon_{p,m} + \epsilon} - 1)}$. These relationships fortify our understanding of the privacy guarantees provided by various mechanisms. Our work not only lays the groundwork for future empirical exploration but also promises to facilitate the design of privacy-preserving algorithms, thereby fostering the development of trustworthy machine learning solutions

Probably Approximately Correct Federated Learning

Federated learning (FL) is a new distributed learning paradigm, with privacy, utility, and efficiency as its primary pillars. Existing research indicates that it is unlikely to simultaneously attain infinitesimal privacy leakage, utility loss, and efficiency. Therefore, how to find an optimal trade-off solution is the key consideration when designing the FL algorithm. One common way is to cast the trade-off problem as a multi-objective optimization problem, i.e., the goal is to minimize the utility loss and efficiency reduction while constraining the privacy leakage not exceeding a predefined value. However, existing multi-objective optimization frameworks are very time-consuming, and do not guarantee the existence of the Pareto frontier, this motivates us to seek a solution to transform the multi-objective problem into a single-objective problem because it is more efficient and easier to be solved. To this end, we propose FedPAC, a unified framework that leverages PAC learning to quantify multiple objectives in terms of sample complexity, such quantification allows us to constrain the solution space of multiple objectives to a shared dimension, so that it can be solved with the help of a single-objective optimization algorithm. Specifically, we provide the results and detailed analyses of how to quantify the utility loss, privacy leakage, privacy-utility-efficiency trade-off, as well as the cost of the attacker from the PAC learning perspective