Human Aspects

The need for accurate and unforgeable identity recognition techniques has become an issue of increasing urgency. Biometric approaches such as iris recognition hold huge promise but still have significant limitations, including susceptibility to 'spoofing'. This project seeks to advance our knowledge of security and accuracy of multibiometric systems by inventing, evaluating, and applying innovative methods and tools to combine highly accurate static traits, such as iris patterns, with novel traits based on the dynamics of eye movements.

In individual pursuits of personalized service and other functionalities, people disclose personal and private information by trusting certain online sites and services. Scholars often assume that such trust is based on a careful assessment of the benefits and risks of disclosing information online. This project departs from such an assumption and investigates the possibility that decision-making about online information disclosure is not systematic, but rather based on cognitive heuristics (or mental shortcuts) triggered by cues in the interaction context.

This project develops a holistic approach to sociotechnical system security that combines innovations in both criminology and engineering/computer science. We design unified sociotechnical security models that capture how sociotechnical intrusions against social as well as technical aspects of the system (i.e., modeled as hidden sequences of system security states) result in observed hard data such as security sensor alerts and soft data produced by human/social sensors such as reports about slow machines.

Public, online harassment takes many forms, but at its core are posts that are offensive, threatening, and intimidating. It is not an isolated problem. The Pew Research Center found 73% of people had witnessed harassment online, and a full 40% of people had experienced harassment directly. This research develops a method for analyzing the things people post online, and automatically detecting which posts fall into the category of severe public online harassment -- messages posted simply to disrupt, offend, or threaten others.

This project closely examines data aggregation to understand what types of aggregation are normatively and descriptively important to individuals and how do different types and degree of aggregation impact individual trust. This proposed research would advance knowledge and understanding within the study of big data, trust, and business ethics. Initial investigations into data aggregation have been technical to ensure accuracy and diminish unwanted bias.