Ektelo is a novel programming framework and system for implementing both existing and new privacy algorithms.
Ektelo is a programming framework and system that aids programmers in developing differentially private programs with high utility. Ektelo can be used to author programs for a variety of statistical tasks that involve answering counting queries over a table of arbitrary dimension.
In Ektelo, a differentially private program is described as a plan over a high level library of operators. Operators are organized into classes based on their functionality, which includes data transformations, data reductions, query selection and execution, and inference methods to combine noisy answers into a consistent estimate.
The operator-based approach has following benefits:
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau. 2018. EKTELO: A Framework for Defining Differentially-Private Computations. In Proceedings of the 2018 International Conference on Management of Data (SIGMOD ‘18). ACM, New York, NY, USA, 115-130. DOI: https://doi.org/10.1145/3183713.3196921
This project is supported by the NSF, DARPA and Center for Data Science at the University of Massachusetts College of Information and Computer Sciences.