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Presentation
Poster: Privacy-Preserving Boosting with Random Linear Classifiers
Kno.e.sis Publications
  • Sagar Sharma, Wright State University - Main Campus
  • Keke Chen, Wright State University
Document Type
Presentation
Publication Date
10-1-2018
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.

Comments

Proceeding CCS '18 Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security

©2018 Copyright held by the owner/author(s).

DOI
0.1145/3243734.3278520
Citation Information
Sagar Sharma and Keke Chen. "Poster: Privacy-Preserving Boosting with Random Linear Classifiers" (2018) p. 2294 - 2296 ISSN: 978-1-4503-5693-0
Available at: http://works.bepress.com/keke_chen/54/