EKTELO: A Framework for Defining Differentially-Private Computations
By Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau. 2018
66 citations recorded by the source corpus.
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, that it 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.
Subfields: Artificial Intelligence, Computer Science Applications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ashwin Machanavajjhala
- Daniel Kifer
- Xi He
- Danfeng Zhang
- Guanhong Wang
- Yuxin Wang
- Zeyu Ding
- Somesh Jha
In Computer Science Applications
- Daniel Kifer
- Danfeng Zhang
- Ashwin Machanavajjhala
- Xi He
- Somesh Jha
- Yuxin Wang
- Guanhong Wang
- Zeyu Ding
Citations in the corpus
- Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers (2019, background): “Like in prior work [25, 13, 29], access to the sensitive data is restricted via a set of predefined transformations operators (inspired by relational algebra) and DP measurement operators (Laplace mechanism and Noisy-Max [12]).”
- Proving differential privacy with shadow execution (2019, background): “, [31, 33, 35, 43]) focuses more on usability – on developing platforms that keep track of the privacy cost of an algorithm while limiting the types of algorithms that users can produce.”