Differentially private multi-party computation

By Peter Kairouz, Sewoong Oh, Pramod Viswanath. 2014

14 citations recorded by the source corpus.

We study the problem of multi-party computation under approximate (ε,δ) differential privacy. We assume an interactive setting with k parties, each possessing a private bit. Each party wants to compute a function defined on all the parties' bits. Differential privacy ensures that there remains uncertainty in any party's bit even when given the transcript of interactions and all the other parties' bits. This paper is a follow up to our work, where we studied multi-party computation under (ε, 0) differential privacy. We generalize the results and prove that a simple non-interactive randomized response mechanism is optimal. Our optimality result holds for all privacy levels (all values of ε and δ), heterogenous privacy levels across parties, all types of functions to be computed, all types of cost metrics, and both average and worst-case (over the inputs) measures of accuracy.

Subfields: Artificial Intelligence

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

1. Y. Yang
2. Xiaokui Xiao
3. Siu Cheung Hui
4. Hyejin Shin
5. Junbum Shin
6. Thông T. Nguyên
7. Ninghui Li
8. Tianhao Wang

Citations in the corpus

• Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy (2016, background): “[15] show that a version of randomized response is an optimal mechanism for frequency estimation on a single binary attribute.”