Jolley Hall, Room 309
Fast, Scalable Systems for Data Privacy
We live in an increasingly networked world with an abundance of personal data stored across multiple (often public) platforms. The absence of strong security mechanisms in place to protect this data ultimately threatens our personal privacy. Data encryption is usually the first line of defense, but in several cases encryption as a standalone solution is simply not enough to protect data privacy. This talk focusses on two critical privacy challenges -- i) privately accessing data on untrusted clouds, and ii) protecting sensitive information against powerful adversaries that can coerce users to reveal encryption keys/passwords. To mitigate these problems, the first part of this talk details a new fast, parallel multi-client access privacy mechanism for data stored on shared untrusted clouds. The second part of this talk discusses several efficient plausibly-deniable storage solutions that allow users to plausibly claim to powerful coercive adversaries that certain sensitive information is not in their possession. This is an important tool in the fight against censorship and oppressive regimes. As a key take-away, this talk highlights the importance of re-thinking foundations and optimizing solutions for metrics that are critical for performance on real hardware (e.g., locality of access) for building efficient and scalable secure systems. In ongoing and future research we are further exploring key performance-security trade-offs and leveraging application-specific requirements to build high-performance secure systems outperforming standard solutions by orders of magnitude.
Anrin Chakraborti is a PhD candidate in Computer Science at Stony Brook University, where he is a member of the National Security Institute (NSI) and the Network Security and Applied Cryptography Lab. His research involves building efficient and provably secure systems to address critical data privacy problems.