Prio3: Private, Robust, and Scalable Computation of Aggregate Statistics and SNIPs

Verifiable Distributed Aggregation Functions (VDAFs)

The collection of aggregated data is one of the most profitable industries in computing, but how can we efficiently harvest data without breaching user privacy? Well, a Multi-party Distributed Aggregation Protocol (DAP) is one answer, and the PRIO3 method is one of the best around for this.

While security has been applied to data while at rest and over the air, we often still do our computations on plaintext data. This opens up many problems, especially that the processor can determine PII (Personally Identifiable Information) and things like secret encryption keys. One approach is to use MPC (Multi-party Computation), and where we can split our computation up into smaller blocks, and give each of the parties a share of the computation. One of the best applications of this is to collect statistics in a privacy-aware manner.

For example, a mobile phone manufacturer may want to determine gender balance for the owners of their phones, but not actually reveal any specific person. For this, each client device creates an SNIP (secret-shared non-interactive proof) for their gender. Within PRIO3, this is a fast and efficient zero-knowledge proof. Then servers can receive these…