Decentralized storage powered by blockchain is becoming a new trend that allows data owners to outsource their data to remote storage resources offered by various storage providers. Unfortunately, unqualified storage providers easily encounter unpredictable downtime due to security threats, such as malicious attacks or system failures, which is unacceptable in many real-time or data-driven applications. As a result, continuous data integrity should be guaranteed in decentralized storage, which ensures that data is intact and available for the entire storage period. However, this requires frequent checking for long time periods and incurs heavy burdens of both communication and computation, especially in a big data scenario. In this paper, we propose an efficient continuous big data integrity checking approach for decentralized storage. We design a data-time sampling strategy that randomly checks the integrity of multiple files at each time slot with high checking probability. Furthermore, to tackle the fairness problem derived from the sampling strategy, we propose a fair approach by designing an arbitration algorithm with the verifiable random function. Security analysis shows the security of our approach under the random oracle model. Evaluation and experiments demonstrate that our approach is more efficient in the big data scenario compared with the state-of-the-arts.

中文翻译：

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去中心化存储的高效连续大数据完整性检查

由区块链驱动的去中心化存储正在成为一种新趋势，它允许数据所有者将他们的数据外包给各种存储提供商提供的远程存储资源。不幸的是，不合格的存储提供商很容易因安全威胁（例如恶意攻击或系统故障）而遇到不可预测的停机时间，这在许多实时或数据驱动的应用程序中是不可接受的。因此，在去中心化存储中应保证持续的数据完整性，以确保数据在整个存储期间完整且可用。然而，这需要长时间的频繁检查，并且带来沉重的通信和计算负担，尤其是在大数据场景中。在本文中，我们提出了一种用于分散存储的高效连续大数据完整性检查方法。我们设计了一种数据时间采样策略，该策略以高检查概率在每个时隙随机检查多个文件的完整性。此外，为了解决采样策略带来的公平性问题，我们通过设计具有可验证随机函数的仲裁算法来提出一种公平方法。安全性分析显示了我们的方法在随机预言机模型下的安全性。评估和实验表明，与现有技术相比，我们的方法在大数据场景中更有效。安全性分析显示了我们的方法在随机预言机模型下的安全性。评估和实验表明，与现有技术相比，我们的方法在大数据场景中更有效。安全性分析显示了我们的方法在随机预言机模型下的安全性。评估和实验表明，与现有技术相比，我们的方法在大数据场景中更有效。