Increasingly, more users and organizations are pushing their data into what is currently known as the "cloud." In such an environment, huge amounts of valuable data are stored and accessible almost ubiquitously within a network. As this trend continues, privacy and access control become increasingly relevant concepts to consider. Unfortunately application developers, service providers, and researchers are only just beginning to understand the challenges associated with the rapidly increasing integration of personal data into the "cloud." Also, much of what has been implemented to provide access control and privacy is based on simple and mostly manual mechanisms. While the amount of data available has grown exponentially and the uses of and services for this data rapidly evolve, the protection mechanisms for this data have changed at a significantly slower pace. This state of affairs does not bode well for those concerned about or tasked with protecting online data. This dissertation addresses this growing challenge through the exploration of new models for privacy and access control that are based on the mental models (e.g., human and social factors) that affect privacy and access control. More specifically, this thesis represents an early attempt to understand and capture the mental processes that people use to make sharing decisions offline as input to access control mechanisms for online environments. The key contributions of this dissertation are a comprehensive exploration of the factors that affect access control in both online and offline environments, system designs and implementations for leveraging these factors for access control and privacy, and survey data and analysis collected about users, their relationships, and their sharing preferences. Это и многое другое вы найдете в книге Searching for Privacy in an Increasingly Public World: Exploits, Experience, and Errors Toward New. Models for Access Control in Online Environments (Lerone D Banks)