A Distributed Trust Framework for Privacy-Preserving Machine Learning

Abstract

When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust between data owners and data scientists. Data owners are justifiably reluctant to relinquish control of private information to third parties. Privacy-preserving techniques distribute computation in order to ensure that data remains in the control of the owner while learning takes place. However, architectures distributed amongst multiple agents introduce an entirely new set of security and trust complications. These include data poisoning and model theft. This paper outlines a distributed infrastructure which is used to facilitate peer-to-peer trust between distributed agents; collaboratively performing a privacy-preserving workflow. Our outlined prototype sets industry gatekeepers and governance bodies as credential issuers. Before participating in the distributed learning workflow, malicious actors must first negotiate valid credentials. We detail a proof of concept using Hyperledger Aries, Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs) to establish a distributed trust architecture during a privacy-preserving machine learning experiment. Specifically, we utilise secure and authenticated DID communication channels in order to facilitate a federated learning workflow related to mental health care data.

Privacy and Trust Redefined in Federated Machine Learning

“…In this paper, we extended our previous work [27] by merging the privacy-preserving ML field with VCs and DIDs while addressing trust concerns within the data industry. These areas focus on people's security, privacy and especially on the protection of their sensitive data.…”

“…The encryption techniques used by DIDComm include ElGamal [52], RSA [53] and elliptic curve-based [54]. Algorithm 1 DID Communication Between Alice and Bob [27] 1: Alice has a private key sk a and a DID Document for Bob containing an endpoint (endpoint bob ) and a public key (pk b ).…”

“…More specifically, in our proofof-concept, we establish trusted connections among only authorised participants and then perform FL over secure communication channels. In our previous work [27], we presented a proof-of-concept that is able to establish trust between the participating parties and perform FL on their data. Thus, to achieve it, we used the basic messages protocol provided by Hyperledger Aries, encoded the ML model and updates into text format and sent it through DIDComm encrypted channels.…”

“…In our implementation, we used the Hyperledger Aries framework to create a distributed FL architecture [27]. The communication between the participating entities takes place through the DIDComm transport protocol.…”

A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric

“…Furthermore, critical medical information must be regularly updated and shared where proper consent is provided by the patient. Along with this we need strong availability, fast access and the appropriate encryption of these records [ 2 , 19 ].…”