Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics
This work addresses privacy concerns in federated analytics for edge devices like smartphones, offering incremental improvements in efficiency and privacy features.
The paper tackles the problem of aggregating private data from edge devices by proposing numerical aggregation protocols that improve upon prior methods while maintaining comparable local differential privacy guarantees, achieving this with a single private bit per value to enable additional privacy controls.
Private data generated by edge devices -- from smart phones to automotive electronics -- are highly informative when aggregated but can be damaging when mishandled. A variety of solutions are being explored but have not yet won the public's trust and full backing of mobile platforms. In this work, we propose numerical aggregation protocols that empirically improve upon prior art, while providing comparable local differential privacy guarantees. Sharing a single private bit per value supports privacy metering that enable privacy controls and guarantees that are not covered by differential privacy. We put emphasis on the ease of implementation, compatibility with existing methods, and compelling empirical performance.