Sharing in a Trustless World: Privacy-Preserving Data Analytics with Potentially Cheating Participants
This addresses the challenge of data sharing and privacy for organizations in collaborative analytics, representing a novel integration of methods rather than a foundational breakthrough.
The paper tackles the problem of enabling privacy-preserving data analytics among mutually mistrusting organizations by proposing DataRing, a system that ensures correctness of input datasets and query answers even with cheating participants, achieving evaluation of 10 queries on a dataset with 500,000 records in 90.63 seconds.
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.