Collaborative data sharing underlies applications in systems such as online social networks and cloud storage. A central provider hosts shared data, e.g., a Facebook group page, and provides sharing users with read/write access according to user-defined settings. Historical incidents prove that data storage centralization enables a corrupted provider to censor or diverge users’ views of the shared data. Such misbehavior is hardly detectable as users update the shared data unbeknown to each other. Existing solutions suggest server-side data replication, which is storage inefficient, or users’ out-of-band communication, which is communication intensive. Addressing these inefficiencies, we propose Integrita, a data-sharing mechanism that protects users’ view-consistency needless to out-of-band communication. We present a novel distributed history tree algorithm to partition the shared data among N storage providers, N−1 of which are Byzantine faulty and colluding. Our data partitioning solution reduces storage overhead by a multiplicative factor of N and allows provable detection of server-side equivocation and identification of corrupted servers. We introduce and achieve a new consistency level, named q-detectable consistency, where users’ views inconsistency cannot remain undetected for more than q updates, q being the system parameters’ function. Without loss of generality, we use online social networks as a case study to deploy Integrita and supply performance/numerical analysis accordingly.

中文翻译：

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Integrita：BFT 分布式存储系统


协作数据共享是在线社交网络和云存储等系统中应用程序的基础。中央提供商托管共享数据，例如 Facebook 群组页面，并根据用户定义的设置为共享用户提供读/写访问权限。历史事件证明，数据存储集中化使损坏的提供商能够审查或分散用户对共享数据的看法。当用户在彼此不知情的情况下更新共享数据时，这种不当行为几乎无法检测到。现有解决方案建议使用服务器端数据复制（存储效率低下）或用户带外通信（通信密集）。为了解决这些低效率问题，我们提出了 Integrita，这是一种数据共享机制，可以保护用户的视图一致性，而无需带外通信。我们提出了一种新的分布式历史树算法，用于在 N 个存储提供者之间对共享数据进行分区，其中 N-1 个是拜占庭错误和串通的。我们的数据分区解决方案将存储开销减少了 N 倍，并允许对服务器端模棱两可的可证明检测和识别损坏的服务器。我们引入并实现了一个新的一致性级别，称为 q-detectable consistency，其中用户的视图不一致不能超过 q 次更新而不被检测到，q 是系统参数的功能。在不失去通用性的情况下，我们使用在线社交网络作为案例研究来部署 Integrita 并相应地提供性能/数值分析。