ϵ KTELO
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, 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 08, 2020
- Source ID
- 10.1145/3362032
Entities
People
- Ashwin Machanavajjhala
- Dan Zhang
- George Bissias
- Gerome Miklau
- Ios Kotsogiannis
- Michael Hay
- Ryan Mckenna
Organizations
- Colgate University
- Defense Advanced Research Projects Agency
- Duke University
- National Science Foundation
- University of Massachusetts