Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

Civil Rights on the Cell Block: Race, Reform, and Violence in Texas Prisons and the Nation, 1945-1990

This study, "Civil Rights on the Cell Block: Race, Reform, and Violence in Texas Prisons and the Nation, 1945-1990," offers a new perspective on the historical origins of the modern prison industrial complex, sexual violence in working-class culture, and the ways in which race shaped the prison experience. This study joins new scholarship that reperiodizes the Civil Rights era while also considering how violence and radicalism shaped the civil rights struggle. It places the criminal justice system at the heart of both an older racial order and within a prison-made civil rights movement that confronted the prison's power to deny citizenship and enforce racial hierarchies. By charting the trajectory of the civil rights movement in Texas prisons, my dissertation demonstrates how the internal struggle over rehabilitation and punishment shaped civil rights, racial formation, and the political contest between liberalism and conservatism. This dissertation offers a close case study of Texas, where the state prison system emerged as a national model for penal management. The dissertation begins with a hopeful story of reform marked by an apparently successful effort by the State of Texas to replace its notorious 1940s plantation/prison farm system with an efficient, business-oriented agricultural enterprise system. When this new system was fully operational in the 1960s, Texas garnered plaudits as a pioneering, modern, efficient, and business oriented Sun Belt state. But this reputation of competence and efficiency obfuscated the reality of a brutal system of internal prison management in which inmates acted as guards, employing coercive means to maintain control over the prisoner population. The inmates whom the prison system placed in charge also ran an internal prison economy in which money, food, human beings, reputations, favors, and sex all became commodities to be bought and sold. I analyze both how the Texas prison system managed to maintain its high external reputation for so long in the face of the internal reality and how that reputation collapsed when inmates, inspired by the Civil Rights Movement, revolted. My dissertation shows that this inmate Civil Rights rebellion was a success in forcing an end to the existing system but a failure in its attempts to make conditions in Texas prisons more humane. The new Texas prison regime, I conclude, utilized paramilitary practices, privatized prisons, and gang-related warfare to establish a new system that focused much more on law and order in the prisons than on the legal and human rights of prisoners. Placing the inmates and their struggle at the heart of the national debate over rights and "law and order" politics reveals an inter-racial social justice movement that asked the courts to reconsider how the state punished those who committed a crime while also reminding the public of the inmates' humanity and their constitutional rights.