Ektelo: A Framework for Defining Differentially-Private Computations

TL;DR

Ektelo is a new programming framework that simplifies the design of differentially-private algorithms, enabling both novices and experts to create accurate, efficient, and safe privacy-preserving computations, especially for linear counting queries.

Contribution

The paper introduces Ektelo, a novel system that supports composing privacy algorithms from operator classes, enhancing expressiveness, safety, and ease of use for differential privacy applications.

Findings

Ektelo can implement nearly all existing algorithms for linear counting queries.

The framework supports the creation of new state-of-the-art privacy algorithms.

Ektelo improves safety and reusability in privacy algorithm development.

Abstract

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, Ektelo, 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 Ektelo system, we show that Ektelo is expressive, allows for safer implementations through code reuse, and that it allows both privacy novices and experts to easily design algorithms. We demonstrate the use of Ektelo by designing several new state-of-the-art algorithms.