We develop statistical inference methods to identify infection sources in a network. Many practical scenarios can be modeled as an infection spreading from one node to another in a network of interconnected nodes. Examples include the spreading of a contagious disease in a community, the propagation of a virus in a computer network, and the spreading of a rumor among participants in a social network. Identifying the sources of an infection plays an important role in many applications, including finding the index cases that introduce a contagious disease into a population network to facilitate epidemiological studies, identifying the servers that inject a computer virus into a computer network so as to determine the latent points of weaknesses in the network, or to apprehend the individuals who started a malicious rumor in a social network. In this work, we develop methods to identify infection sources and jointly detect the infection spreading.

With the ubiquitous adoption of Internet of Things (IoT) devices like on-body sensors, smart home appliances, and smart phones, massive amounts of data about users’ habits, routines and preferences are being collected by service providers. While such data are utilized by service providers to improve the quality of life, e.g., by making building heating and ventilation systems more intelligent and adaptive, the same data can also be exploited to learn users’ private behaviors, habits and lifestyle choices. For consumers to widely adopt IoT systems, privacy protection mechanisms are a necessary feature of future IoT products. An example is the deployment of home-monitoring video cameras in old folks' homes for fall detection. If the cameras transmit the raw video feed to a fusion center, the fusion center can not only use these video feeds for fall detection, but also has the potential to intrude on the privacy of the home inhabitants. The camera sensors therefore need to perform intelligent observation summary with a suitable privacy mapping in order to limit the amount and quality of information they send to the fusion center. In this project, we investigate the concept of information privacy for IoT systems, as opposed to data privacy, and develop information privacy-aware inference algorithms for IoT systems.

Social learning is the use of social networks (including online networks like Facebook and Twitter, and physical networks formed using an ad hoc mesh of smart phones) to perform event detection and inference. We develop a mathematical framework for robust learning in social networks, study the fundamental learning accuracy achievable in such networks, and propose methods for efficient robust social learning in the presence of misinformation and malicious agents. We aim to bridge the gaps in our current understanding of how learning or inference is impacted by misinformation or malicious agents in a social network. On the other hand, in a crowdsourcing platform workers’ responses to questions posed by a crowdsourcer are used to determine the hidden state of a multi-class labeling problem. As workers may be unreliable, we propose to perform sequential questioning in which the questions posed to the workers are designed based on previous questions and answers.