Abstract
With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality.
Highlights
Data-driven methods, e.g., machine learning and data mining, have become essential tools for numerous research and application domains
The cost of external storage and related I/O operations are critical to the cloud-side components as they are responsible for storing the encrypted data, which often is much larger than the plaintext version and cannot reside in memory
When Garbled Circuits (GC) is adopted as a primitive to implement some components, additional communication cost related to the GC protocol is significant, including the cost of transmitting the circuit and one-party’s input data obliviously to the other party (Liu et al 2015; Huang et al 2011)
Summary
Data-driven methods, e.g., machine learning and data mining, have become essential tools for numerous research and application domains. Limited in-house resources, inadequate expertise, or collaborative/distributed processing needs force data owners (e.g., parties that collect and analyze user-generated data) to depend on somewhat untrusted platforms (e.g., cloud/edge service providers) for elastic storage and data processing. Google Cloud Platform has allowed users to include an external key manager to store encrypted data on the cloud with a third party (e.g., Fortanix) stores and manages keys off the cloud. It remains a critical challenge for data owners and cloud providers to protect confidentiality in computing, i.e., training models on (2021) 4:30 the cloud, while protecting the confidentiality of both the training data and the learned models
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