Document Type
Presentation
Publication Date
10-2018
Abstract
We propose SecureBoost, a privacy-preserving predictive modeling framework, that allows service providers (SPs) to build powerful boosting models over encrypted or randomly masked user submit- ted data. SecureBoost uses random linear classifiers (RLCs) as the base classifiers. A Cryptographic Service Provider (CSP) manages keys and assists the SP’s processing to reduce the complexity of the protocol constructions. The SP learns only the base models (i.e., RLCs) and the CSP learns only the weights of the base models and a limited leakage function. This separated parameter holding avoids any party from abusing the final model or conducting model-based attacks. We evaluate two constructions of SecureBoost: HE+GC and SecSh+GC using combinations of primitives - homomorphic encryption, garbled circuits, and random masking. We show that SecureBoost efficiently learns high-quality boosting models from protected user-generated data with practical costs.
Repository Citation
Sharma, S.,
& Chen, K.
(2018). Poster: Privacy-Preserving Boosting with Random Linear Classifiers. , 2294-2296.
https://corescholar.libraries.wright.edu/knoesis/1158
DOI
0.1145/3243734.3278520
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
Proceeding CCS '18 Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
©2018 Copyright held by the owner/author(s).