Paper 2018/289
Private Nearest Neighbors Classification in Federated Databases
Phillipp Schoppmann and Adrià Gascón and Borja Balle
Abstract
Privacy-preserving data analysis in the context of federated databases distributed across multiple parties has the potential to produce richer and more accurate models than what each party can learn with their own data. Secure Multi-Party Computation (MPC) offers a robust cryptographic approach to this problem, and in fact several protocols have been proposed for various learning tasks on parametric models. In this paper we focus on $k$-NN, shifting the attention towards non-parametric models. We tackle several challenges arising in privacy-preserving $k$-NN classification on federated databases, and implement a concrete protocol for document classification. Our solution is faster than the state-of-the-art custom MPC protocol by at least one an order of magnitude.
Metadata
- Available format(s)
- Category
- Cryptographic protocols
- Publication info
- Preprint. MINOR revision.
- Keywords
- Multi-Party ComputationText ClassificationMachine LearningNon-Parametric Modelsk-Nearest-Neighbors
- Contact author(s)
- schoppmann @ informatik hu-berlin de
- History
- 2020-04-18: revised
- 2018-03-28: received
- See all versions
- Short URL
- https://ia.cr/2018/289
- License
-
CC BY