Accelerating 2PC-based ML with Limited Trusted Hardware

09/11/2020
by   Muqsit Nawaz, et al.
0

This paper describes the design, implementation, and evaluation of Otak, a system that allows two non-colluding cloud providers to run machine learning (ML) inference without knowing the inputs to inference. Prior work for this problem mostly relies on advanced cryptography such as two-party secure computation (2PC) protocols that provide rigorous guarantees but suffer from high resource overhead. Otak improves efficiency via a new 2PC protocol that (i) tailors recent primitives such as function and homomorphic secret sharing to ML inference, and (ii) uses trusted hardware in a limited capacity to bootstrap the protocol. At the same time, Otak reduces trust assumptions on trusted hardware by running a small code inside the hardware, restricting its use to a preprocessing step, and distributing trust over heterogeneous trusted hardware platforms from different vendors. An implementation and evaluation of Otak demonstrates that its CPU and network overhead converted to a dollar amount is 5.4-385× lower than state-of-the-art 2PC-based works. Besides, Otak's trusted computing base (code inside trusted hardware) is only 1,300 lines of code, which is 14.6-29.2× lower than the code-size in prior trusted hardware-based works.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/31/2021

Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support

Confidential multi-stakeholder machine learning (ML) allows multiple par...
research
09/25/2020

Walnut: A low-trust trigger-action platform

Trigger-action platforms are a new type of system that connect IoT devic...
research
10/01/2018

Privado: Practical and Secure DNN Inference

Recently, cloud providers have extended support for trusted hardware pri...
research
02/13/2018

DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation

It is safe to assume that, for the foreseeable future, machine learning,...
research
11/29/2021

Third-Party Hardware IP Assurance against Trojans through Supervised Learning and Post-processing

System-on-chip (SoC) developers increasingly rely on pre-verified hardwa...
research
05/11/2019

Artificial Consciousness and Security

This paper describes a possible way to improve computer security by impl...
research
11/24/2022

Beyond Mahalanobis-Based Scores for Textual OOD Detection

Deep learning methods have boosted the adoption of NLP systems in real-l...

Please sign up or login with your details

Forgot password? Click here to reset