Division of Information & Intelligent Systems (IIS)

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.

Security and privacy play a key role in areas such as health, energy, and smart cities, as well as constituting a grand challenge in and of themselves. Privacy and security are critical for realizing Big Data's promise to advance society. If data are used without regard to privacy of individuals or protection of the data, then individuals may be hurt. If the data's authenticity is not guaranteed, or if data are not permitted to be used at all due to privacy and security concerns, then the data's value is not realized.