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.

中文翻译：

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联合分析中的比特高效数值聚合和更强的信任隐私

边缘设备（从智能手机到汽车电子设备）生成的私人数据在汇总时具有很高的信息量，但在处理不当时可能会造成破坏。各种解决方案正在探索中，但尚未赢得公众对移动平台的信任和全力支持。在这项工作中，我们提出了数值聚合协议，该协议从经验上改进了现有技术，同时提供了可比较的本地差分隐私保证。每个值共享一个私有位支持隐私计量，从而启用隐私控制和保证，这些隐私不受差异隐私保护。我们强调易于实施、与现有方法的兼容性以及令人信服的经验表现。