Elissa M. Redmiles

Learn More
Users receive a multitude of digital-and physical-security advice every day. Indeed, if we implemented all the security advice we received, we would never leave our houses or use the Internet. Instead, users selectively choose some advice to accept and some (most) to reject, however, it is unclear whether they are effectively prioritizing what is most(More)
Few users have a single, authoritative, source from whom they can request digital-security advice. Rather, digital-security skills are often learned haphazardly, as users filter through an overwhelming quantity of security advice. By understanding the factors that contribute to users' advice sources, beliefs, and security behaviors, we can help to pare down(More)
Usable security researchers have continuously explored social and demographic factors, and even beliefs, that affect user security behavior. However, no formal study of the relationship between internet skill and security behavior has been conducted. In this poster, we present a survey of 102 Amazon Mechanical Turk workers. We find small, but significant,(More)
The behavior of the least-secure user can influence security and privacy outcomes for everyone else. Thus, it is important to understand the factors that influence the security and privacy of a broad variety of people. Prior work has suggested that users with differing socioeconomic status (SES) may behave differently; however, no research has examined how(More)
International development banks provide low-interest loans to developing countries in an effort to stimulate social and economic development. These loans support key infrastructure projects including the building of roads, schools, and hospitals. However, despite the best efforts of development banks, these loan funds are often lost to fraud, corruption,(More)
Cheap and readily available storage systems help us gather hundreds of terabytes of data everyday. This information can be analyzed to understand and predict radical human behavior and actions in order to mitigate loss of lives and property. We propose using machine learning method on CCTV, audio, and geotagged Twitter data to identify suspicious persons(More)
In 2012, women earned 18% of computer science degrees; African American and Hispanic students made up less than 20% of computing degree holders that year. Research shows that relatable role models and engaging curriculum are required to engage underrepresented students in computing. There is a need for engaging and relatable curriculum to be delivered to(More)
Mentors-protégé relationships have been shown to improve retention of women and under-represented students in computing (Cohoon, 2011). Mentorship relationships are also the driving factor in female students' selection and completion of a computing career (Ashcraft, Eger, & Friend, 2012). More generally, mentor-protégé relationships(More)
Taxi sharing services are increasingly becoming important to meet rising accessibility needs and maintain stable income-earning societies with rising social capital [1]. Mobile platforms for taxi sharing are currently in development both commercially and within academic institutions. The success of these platforms relies not only on robust algorithms but(More)
  • 1