Existing protocol analysis are typically confined to the electronic messages exchanged among computer systems running at the endpoints. In this project we take a broader view in which a protocol additionally encompasses both physical technologies as well as human participants. Our goal is to develop techniques for analyzing and proving security of protocols involving all these entities, with open-audit, remote voting systems such as Remotegrity as our starting point.

Our goal is to develop a transormational framework for a science of trust, and its impact on local policies for collaboration, in networked multi-agent systems. The framework will take human bahavior into account from the start by treating humans as integrated components of these networks, interacting dynamically with other elements. The new analytical framework will be integrated, and validated, with empirical methods of analyzing experimental data on trust, recommendation and reputation, from several datasets available to us, in order to capture fundamental trends and patterns of human behavior, including trust and mistrust propagation, confidence in trust, phase transitions in the dynamic graph models involved in the new framework, stability or instability of collaborations.

Trust as a concept, has been developed and used in several settings and in various forms. It has been devloped and applied in social and economic networks as well as information and communication networks. An important challenge is the diversity of descriptions and uses of trust that have appeared in prior work. Another challenge is the relative scarcity of quantitative and formal methods for modeling and evaluating trust. Methods for modeling trust have varied from simple empirical models based on statistical experiments, to simple scalar weights, to more sophisticated policy-based methods. Furthermore, there are very few works attempting to link empirical data on trust (in particular data on human behavior) to various formal and quantitative models.

Our new framework is based on our recently developed foundational model for networked multi-agent systems in which we consider three interacting dynamic graphs on the same underlying set of nodes: a social/agent network, which is relational; an information network, which is also relational; and a communication network that is physical. These graphs are directed and their links and nodes are annotated with dynamically changing "weights" representing trust metrics whose formal definition and mathematical representation can take one of several options, e.g. weights can be scalars, vectors, or even policies (i.e. rules). Such models, in much simpler mathematical form, have been used in social- and economic-network studies under the name of value directed graphs. The model we are developing is far more sophisticated, and thus much more expressive. We will incorporate within such models complex human behavior in various forms.

Within this new framework that we are developing, we are specifically focusing on investigating the following fundamental problems: (a) Theories and principles governing the spreading dynamics of trust and msitrust among memebers of a network; (b) Design and analysis of recommendation systems, their dynamics and integrity; (c) Development of a framework for understanding the composition of trust across various networks at the different layers of our basic model; (d) Analysis of the effects of trust on collaboration in networked multi-agent systems, using game-theoretic and economic principles.

Various practical applications are also pursued to demonstrate the results in various practical settings.

In these investigations we principally use the following analytical methods and appropriate extensions: (i) Multiple partially ordered semirings; (ii) Constrained-coalitional games on dynamic networks; (iii) Embeddings of complex annotated graphs in nonlinear parametric spaces for the development of scalable and fast algorithms (e.g. hyperbolic networks and hyperbolic embeddings); (iv) Sophisticated statistical analysis of experimental data on trust and associated human behavioral patterns.

Cloud and mobile computing creates new platforms where applications developed by third-party vendors can access users' devices and computer users' private data. Examples include iPhone and Android apps, and cloud-based application marketplaces.  This project is a synergistic effort combining social behavioral science and secure software systems design. The first thrust of the project seeks to understand users' privacy expectations for their private data, and how the privacy policies vary in different social contexts. With this understanding, we will investigate how to build a platform such that 1) app developers can develop applications that respect users' privacy without being security experts; and 2) the system can understand and enforce users' fine-grained privacy policies, with minimal interruptions to a user's normal workflow. The second thrust of the project seeks to understand how developers make decisions about incorporating privacy and security features into applications, and test interventions to encourage data protection. This project will ask:  1. What encourages developers to adopt new privacy and security practices? 2. How do mobile application developers make choices between privacy, security and other priorities? 3. How can interventions (such as education, availability of best practices, or new software tools) encourage privacy and security by design?

Human choice and behavior are critical to the effectiveness of many security systems; unfortunately, security designers often take little consideration of user preferences, perceptions, abilities, and usability workflow. To address these challenges, we propose research on the user-centric design of security applications, and the development of new usable-security measurement techniques and metrics to inform the design and development of new cybersecurity applications. We will focus on two primary tasks: (1) Empirical measurments of human behavior, the gathering of empirical data of about human behavior vis-a-vis cyber security systems; and, (2) Developing user-based security and usability metrics, the development of new metrics for measuring security based on user perception of security-usability using data collected from empirical studies. 

More appropriate and efficient security solutions against system trespassing incidents can be developed once the attack threat is better understood. However, few empirical studies exist to assess the attack threat. Our proposed research applies “soft science” models (i.e. sociological psychological and criminological) in effort to better understand the threat of system trespassing. The proposed research will draw on data collected on attackers who gain illegitimate access to computers by finding the correct combination username/password on SSH to a computer running Unix, during a randomized experiment. Once an attacker has access to the computer, he/she can build the attack over a period of 30 days. Previous research has shown that a warning banner does not have an effect when attackers launch an attack but does when deciding which computer to use to develop an attack.

Past studies have shown that vulnerabilities in software are often exploited for years after the existence of the vulnerability is disclosed. Our project will leverage Symantec's WINE data set to understand the rate at which vulnerabilities are patched and how the number of affected machines changes over time. We will also conduct a study with system administrators to statistically investigate various hypotheses related to how sys-admins prioritize which vulnerabilities to patch. Finally, we are conducting user studies to determine the reasons why users choose to patch software and examine whether this qualitative data is supported by the WINE data set. Our goal is to develop guidelines to improve the rate of patching from both the technical and user perspectives.