Bringing Private Reads to Hyperledger Fabric via Private Information Retrieval

TL;DR

This paper introduces a PIR-based private read mechanism for Hyperledger Fabric, enabling encrypted queries to protect sensitive data while maintaining blockchain integrity, with promising performance metrics and scalability insights.

Contribution

It presents the first implementation of PIR-enabled private reads in Hyperledger Fabric, integrating ciphertext-plaintext homomorphic multiplication within chaincode.

Findings

Achieves 113 ms average latency for private reads

Peer-side execution time below 42 ms

Supports up to 512 records of 64 bytes each in large configurations

Abstract

Permissioned blockchains ensure integrity and auditability of shared data but expose query parameters to peers during read operations, creating privacy risks for organizations querying sensitive records. This paper proposes a Private Information Retrieval (PIR) mechanism to enable private reads from Hyperledger Fabric's world state, allowing endorsing peers to process encrypted queries without learning which record is accessed. We implement and benchmark a PIR-enabled chaincode that performs ciphertext-plaintext (ct-pt) homomorphic multiplication directly within evaluate transactions, preserving Fabric's endorsement and audit semantics. The prototype achieves an average end-to-end latency of 113 ms and a peer-side execution time below 42 ms, with approximately 2 MB of peer network traffic per private read in development mode--reducible by half under in-process deployment. Storage profiling across three channel configurations shows near-linear growth: block size increases from 77 kilobytes to 294 kilobytes and world-state from 112 kilobytes to 332 kilobytes as the ring dimension scales from 8,192 to 32,768 coefficients. Parameter analysis further indicates that ring size and record length jointly constrain packing capacity, supporting up to 512 records of 64 bytes each under the largest configuration. These results confirm the practicality of PIR-based private reads in Fabric for smaller, sensitive datasets and highlight future directions to optimize performance and scalability.