Privacy-Preserving Boosting with Random Linear Classifiers

TitlePrivacy-Preserving Boosting with Random Linear Classifiers
Publication TypeConference Poster
Year of Publication2018
AuthorsSagar Sharma, Keke Chen
Date Published10/2018
Place PublishedACM Conference on Computer and Communications Security (CCS) 2018
Publication Languageeng
KeywordsBoosting, Privacy-preserving
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.

Citation Key2920
Full Text

Citation:
Sagar Sharma and Keke Chen, Privacy-Preserving Boosting with Random Linear Classifiers, ACM CCS Poster Session, Toronto 2018

Related Files: