This article proposes a new architecture for privacy-preserving data mining based on Multi Party Computation (MPC) and secure sums. While traditional MPC approaches rely on a small number of aggregation peers replacing a centralized trusted entity, the current study puts forth a distributed solution that involves all data sources in the aggregation process, with the help of a single server for storing intermediate results. A large-scale scenario is examined and the possibility that data become inaccessible during the aggregation process is considered, a possibility that traditional schemes often neglect. Here, it is explicitly examined, as it might be provoked by intermittent network connectivity or sudden user departures. For increasing system reliability, data sources are organized in multiple sets, called rings, which independently work on the aggregation process. Two different protocol schemes are proposed and their failure probability, i.e., the probability that the data mining output cannot guarantee the desired level of accuracy, is analytically modeled. The privacy degree, the communication cost and the computational complexity that the schemes exhibit are also characterized. Finally, the new protocols are applied to some specific use cases, demonstrating their feasibility and attractiveness.

中文翻译：

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Rings for Privacy：大规模隐私保护数据挖掘的架构

本文提出了一种基于多方计算 (MPC) 和安全总和的隐私保护数据挖掘新架构。传统的 MPC 方法依赖于少数聚合对等点代替集中式可信实体，而当前的研究提出了一种分布式解决方案，该解决方案涉及聚合过程中的所有数据源，并借助单个服务器来存储中间结果。检查大规模场景并考虑在聚合过程中数据变得不可访问的可能性，这是传统方案经常忽略的可能性。在这里，它被明确检查，因为它可能是由间歇性网络连接或突然用户离开引起的。为了提高系统可靠性，数据源被组织成多个集合，称为环，它们独立地处理聚合过程。提出了两种不同的协议方案，并对它们的失败概率，即数据挖掘输出不能保证所需精度水平的概率进行了分析建模。还表征了方案展示的隐私度、通信成本和计算复杂度。最后，新协议应用于一些特定的用例，展示了它们的可行性和吸引力。