G. Cormode and I. Markov. Bit-efficient numerical aggregation and stronger privacy for trust in federated analytics. In PPML Workshop, 2021.

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.

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