Private data generated by edge devices -- from smart phones to automotive\nelectronics -- are highly informative when aggregated but can be damaging when\nmishandled. A variety of solutions are being explored but have not yet won the\npublic's trust and full backing of mobile platforms. In this work, we propose\nnumerical aggregation protocols that empirically improve upon prior art, while\nproviding comparable local differential privacy guarantees. Sharing a single\nprivate bit per value supports privacy metering that enable privacy controls\nand guarantees that are not covered by differential privacy. We put emphasis on\nthe ease of implementation, compatibility with existing methods, and compelling\nempirical performance.\n