Data science

Information about individuals is collected by a variety of organizations including government agencies, banks, hospitals, research institutions, and private companies. In many cases, sharing this data among organizations can bring benefits in social, scientific, business, and security domains, as the collected information is of similar nature, of about similar populations. However, much of this collected data is sensitive as it contains personal information, or information that could damage an organization's reputation or competitiveness.

Machine learning on large-scale patient medical records can lead to the discovery of novel population-wide patterns enabling advances in genetics, disease mechanisms, drug discovery, healthcare policy, and public health. However, concerns over patient privacy prevent biomedical researchers from running their algorithms on large volumes of patient data, creating a barrier to important new discoveries through machine-learning. The goal of this project is to address this barrier by developing privacy-preserving tools to query, cluster, classify and analyze medical databases.

Information technology, advances in statistical computing, and the deluge of data available through the Internet are transforming computational social science. However, a major challenge is maintaining the privacy of human subjects. This project is a broad, multidisciplinary effort to help enable the collection, analysis, and sharing of sensitive data while providing privacy for individual subjects.

Data provenance refers to the history of the contents of an object and its successive transformations. Knowledge of data provenance is beneficial to many ends, such as enhancing data trustworthiness, facilitating accountability, verifying compliance, aiding forensics, and enabling more effective access and usage controls. Provenance data minimally needs integrity assurance to realize these benefits.

Used by hundreds of millions of people every day, online services are central to everyday life. Their popularity and impact make them targets of public opinion skewing attacks, in which those with malicious intent manipulate the image of businesses, mobile applications and products. Website owners often turn to crowdsourcing sites to hire an army of professional fraudsters to paint a fake flattering image for mediocre subjects or trick people into downloading malicious software.