We propose the first method that realizes the Laplace mechanism exactly (i.e., a Laplace noise is added to the data) that requires only a finite amount of communication (whereas the original Laplace mechanism requires the transmission of a real number) while guaranteeing privacy against the server and database. Our mechanism can serve as a drop-in replacement for local or centralized differential privacy applications where the Laplace mechanism is used. Our mechanism is constructed using a random quantization technique. Unlike the simple and prevalent Laplace-mechanism-then-quantize approach, the quantization in our mechanism does not result in any distortion or degradation of utility. Unlike existing dithered quantization and channel simulation schemes for simulating additive Laplacian noise, our mechanism guarantees privacy not only against the database and downstream, but also against the honest but curious server which attempts to decode the data using the dither signals.

翻译：我们提出首个能够精确实现拉普拉斯机制（即向数据中添加拉普拉斯噪声）且仅需有限通信量（原始拉普拉斯机制需传输实数）的方法，同时确保对服务器和数据库的隐私保护。该机制可直接替代局部或集中式差分隐私应用中采用拉普拉斯机制的方案。本机制基于随机量化技术构建。与简单且普遍的"先拉普拉斯机制后量化"方法不同，本机制中的量化不会导致任何效用失真或退化。与现有用于模拟加性拉普拉斯噪声的抖动量化和信道模拟方案不同，本机制不仅保证对数据库和下游的隐私保护，还能防范试图利用抖动信号解码数据的"诚实但好奇"的服务器。