# Ektelo: A Framework for Defining Differentially-Private Computations

arXiv: https://arxiv.org/abs/1808.03555 · HTML: https://mlsanity.com/paper/1808.03555/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Hermite Polynomial Features for Private Data Generation](https://mlsanity.com/paper/2106.05042/) (2021)
- [Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming](https://mlsanity.com/paper/2104.01808/) (2021)
- [Relaxed Marginal Consistency for Differentially Private Query Answering](https://mlsanity.com/paper/2109.06153/) (2021)
- [Accurate and Scalable Matrix Mechanisms via Divide and Conquer](https://mlsanity.com/paper/2604.00868/) (2026)

## Abstract (arXiv)

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.