Protect

Modern cryptography offers an impressive virtual buffet to a consumer who is wealthy in resources, with powerful tools like fully homomorphic encryption (which allows a provider to compute with encrypted values while keeping the client's data safe) and general purpose obfuscation (which allows one to hide the purpose of a given computation). But for more modestly minded users, who seek to perform less lofty tasks using more affordable computing resources or under more time-tested assumptions, the offerings are comparatively paltry.

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.

Malicious software (a.k.a. malware) is at the basis of most cyber-criminal operations, causing significant financial loss and posing great risks to national security.

Many compelling applications involve computations that require sensitive data from two or more individuals. For example, as the cost of personal genome sequencing rapidly plummets many genetics applications will soon be within reach of individuals such as comparing one?s genome with the genomes of different groups of participants in a study to determine which treatment is likely to be most effective. Such comparisons could have tremendous value, but are currently infeasible because of the privacy concerns both for the individual and study participants.

When data is outsourced at a cloud storage provider, data owners lose control over the integrity of their data and must trust the storage provider unconditionally. Coupled with numerous data loss incidents, this prevents organizations from assessing the risk posed by outsourcing data to untrusted clouds, making cloud storage unsuitable for applications that require long-term security and reliability guarantees. This project establishes a practical remote data checking (RDC) framework as a mechanism to provide long-term integrity and reliability for remotely stored data.