ϵ KTELO

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, ϵ 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. After describing the design and architecture of the ϵ KTELO system, we show that ϵ KTELO is expressive, allows for safer implementations through code reuse, and allows both privacy novices and experts to easily design algorithms. We provide a number of novel implementation techniques to support the generality and scalability of ϵ KTELO operators. These include methods to automatically compute lossless reductions of the data representation, implicit matrices that avoid materialized state but still support computations, and iterative inference implementations that generalize techniques from the privacy literature. We demonstrate the utility of ϵ KTELO by designing several new state-of-the-art algorithms, most of which result from simple re-combinations of operators defined in the framework. We study the accuracy and scalability of ϵ KTELO plans in a thorough empirical evaluation.