Sharing in a Trustless World: Privacy-Preserving Data Analytics with Potentially Cheating Participants

TL;DR

DataRing enables privacy-preserving data sharing among mistrusting parties, ensuring correctness and detecting cheating in joint data analysis using cryptography and differential privacy, demonstrated on large datasets.

Contribution

Introduces DataRing, a system combining cryptography and differential privacy to enable trustless, privacy-preserving data sharing with cheating detection capabilities.

Findings

Achieves query evaluation in approximately 90 seconds for large datasets.

Effectively detects cheating participants with high accuracy.

Maintains individual privacy and data confidentiality.

Abstract

Lack of trust between organisations and privacy concerns about their data are impediments to an otherwise potentially symbiotic joint data analysis. We propose DataRing, a data sharing system that allows mutually mistrusting participants to query each others' datasets in a privacy-preserving manner while ensuring the correctness of input datasets and query answers even in the presence of (cheating) participants deviating from their true datasets. By relying on the assumption that if only a small subset of rows of the true dataset are known, participants cannot submit answers to queries deviating significantly from their true datasets. We employ differential privacy and a suite of cryptographic tools to ensure individual privacy for each participant's dataset and data confidentiality from the system. Our results show that the evaluation of 10 queries on a dataset with 10 attributes and 500,000 records is achieved in 90.63 seconds. DataRing could detect cheating participant that deviates from its true dataset in few queries with high accuracy.