Prio: Private, Robust, and Scalable Computation of Aggregate Statistics
Speaker
Henry Corrigan-Gibbs
Dept. of Computer Science, Stanford University
Host
CSAIL Security Seminar
TITLE
"Prio: Private, Robust, and Scalable Computation
of Aggregate Statistics"
ABSTRACT
This talk will present Prio, a privacy-preserving system for the collection of aggregate statistics. Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location). As long as at least one server is honest, the Prio servers learn nearly nothing about the clients' private data, except what they can infer from the aggregate statistics that the system computes. To protect functionality in the face of faulty or malicious clients, Prio uses secret-shared non-interactive proofs (SNIPs), a new cryptographic technique that yields a hundred-fold performance improvement over conventional zero-knowledge approaches. Prio extends classic private aggregation techniques to enable the collection of large class of useful statistics. For example, Prio can perform a least-squares regression on high-dimensional client-provided data without ever seeing the data in the clear.
This is joint work with Dan Boneh. Our paper on Prio is to appear at NSDI 2017.
BIO
Henry Corrigan-Gibbs is a fourth-year PhD student in computer science at Stanford, advised by Dan Boneh. His work uses cryptographic techniques to bring rigorous privacy properties to large-scale computer systems. For these research efforts, Henry and his co-authors have received the 2015 IEEE Security and Privacy Distinguished Paper Award and the 2016 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies. He is the recipient of an NSF Graduate Research Fellowship and an NDSEG Fellowship.
"Prio: Private, Robust, and Scalable Computation
of Aggregate Statistics"
ABSTRACT
This talk will present Prio, a privacy-preserving system for the collection of aggregate statistics. Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location). As long as at least one server is honest, the Prio servers learn nearly nothing about the clients' private data, except what they can infer from the aggregate statistics that the system computes. To protect functionality in the face of faulty or malicious clients, Prio uses secret-shared non-interactive proofs (SNIPs), a new cryptographic technique that yields a hundred-fold performance improvement over conventional zero-knowledge approaches. Prio extends classic private aggregation techniques to enable the collection of large class of useful statistics. For example, Prio can perform a least-squares regression on high-dimensional client-provided data without ever seeing the data in the clear.
This is joint work with Dan Boneh. Our paper on Prio is to appear at NSDI 2017.
BIO
Henry Corrigan-Gibbs is a fourth-year PhD student in computer science at Stanford, advised by Dan Boneh. His work uses cryptographic techniques to bring rigorous privacy properties to large-scale computer systems. For these research efforts, Henry and his co-authors have received the 2015 IEEE Security and Privacy Distinguished Paper Award and the 2016 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies. He is the recipient of an NSF Graduate Research Fellowship and an NDSEG Fellowship.