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.

翻译：各组织之间缺乏信任,而且对其数据缺乏隐私方面的担心,阻碍了对可能具有共生性的联合数据进行分析。我们提议数据Ring,这是一个数据共享系统,使互不信任的参与者能够以隐私保护的方式相互查询对方的数据集,同时确保输入数据集和查询答案的正确性,即使在偏离其真实数据集的参与者(切换者)在场的情况下,也确保输入数据集和查询答案的正确性。我们依据的假设是,如果只知道真实数据集的一行中的一小部分,参与者无法对与其真实数据集大相径庭的查询提出答案。我们采用了不同的隐私和一套加密工具,以确保每个参与者的数据集和系统的数据保密性。我们的结果显示,对10个数据集的查询和50万个记录在90.63秒内得到评估。数据Ring可以检测出在少数查询中偏离其真实数据集的欺骗性参与者。