The adoption of differential privacy is growing but the complexity of designing private, efficient and accurate algorithms is still high. We propose a novel programming framework and system, ∈ktelo, for implementing both existing and new privacy algorithms. For the task of answering linear counting queries, we show that nearly all existing algorithms can be composed from operators, each conforming to one of a small number of operator classes. While past programming frameworks have helped to ensure the privacy of programs, the novelty of our framework is its significant support for authoring accurate and efficient (as well as private) programs. We describe the design and architecture of the ∈ktelo system and show that ∈ktelo is expressive enough to describe many algorithms from the privacy literature. ∈ktelo allows for safer implementations through code reuse and allows both privacy novices and experts to more easily design new algorithms. We demonstrate the use of ∈ktelo by designing new algorithms offering state-of-the-art accuracy and runtime.

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差分隐私的采用正在增长，但设计私有、高效和准确算法的复杂性仍然很高。我们提出了一种新颖的编程框架和系统 ∈ktelo，用于实现现有和新的隐私算法。对于回答线性计数查询的任务，我们表明几乎所有现有算法都可以由运算符组成，每个运算符都符合少数运算符类别之一。虽然过去的编程框架有助于确保程序的隐私，但我们框架的新颖之处在于它对编写准确和高效（以及私有）程序的重要支持。我们描述了 ∈ktelo 系统的设计和架构，并表明 ∈ktelo 的表达能力足以描述隐私文献中的许多算法。∈ktelo 允许通过代码重用实现更安全的实现，并允许隐私新手和专家更轻松地设计新算法。我们通过设计提供最先进的准确性和运行时间的新算法来演示 ∈ktelo 的使用。