Verifiable Distributed Aggregation Functions

ePrint Report: Verifiable Distributed Aggregation Functions

Hannah Davis, Christopher Patton, MIke Rosulek, Phillipp Schoppmann

The modern Internet is built on systems that incentivize collection of information about users. In order to minimize privacy loss, it is desirable to prevent these systems from collecting more information than is required for the application. The promise of multi-party computation is that data can be aggregated without revealing individual measurements to the data collector. This work offers a provable security treatment for "Verifiable Distributed …

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