Report Noisy Max with Gap 机制的浮点安全实现
A Floating-Point Secure Implementation of the Report Noisy Max with Gap Mechanism
AI总结：
针对 Noisy Top-k with Gap 的隐私保证依赖无限精度的问题，本文利用整数算术提出一种有限精度的安全实现。
摘要：
Noisy Max 机制及其变体是基础的私有选择算法，用于从一组候选对象中选择项目（例如人群中最常见的疾病），同时控制底层数据中的隐私泄露。最近提出的扩展 Noisy Top-k with Gap 提供了数值信息，说明被选择项目相对于未被选择项目好多少（例如，被选择疾病的常见程度高多少）。这些额外信息不会带来隐私成本，但其隐私保证关键依赖于无限精度。本文中，我们利用整数算术给出了该算法的有限精度安全实现。
英文摘要：
The Noisy Max mechanism and its variations are fundamental private selection algorithms that are used to select items from a set of candidates (such as the most common diseases in a population), while controlling the privacy leakage in the underlying data. A recently proposed extension, Noisy Top-k with Gap, provides numerical information about how much better the selected items are compared to the non-selected items (e.g., how much more common are the selected diseases). This extra information comes at no privacy cost but crucially relies on infinite precision for the privacy guarantees. In this paper, we provide a finite-precision secure implementation of this algorithm that takes advantage of integer arithmetic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
