Data integrity is a core security issue in reliable cloud storage that has received widespread attention. Data auditing protocols enable verifiers to efficiently check the integrity of outsourced data without downloading the data. A key research challenge associated with the design of existing data auditing protocols is the efficiency of the auditing process. Since existing protocols tend to auditing all cloud data, in fact, some data may have just been used or will be used soon, then auditing all these data is invalid, which is a waste of resources. In this paper, we attempt to address the waste of resources due to invalid auditing in cloud data integrity checking by introducing user behavior prediction algorithms,the first in such an approach, to the best of our knowledge. More specifically, we introduce the concept of valid auditing in data integrity verification based on the concept of valid auditing. We formalize a system model and a security model for this new concept. Then, using user behavior prediction algorithms, we give methods to improve auditing efficiency and reduce invalid auditing. Finally, we develop a prototype implementation of the protocol to validate the practicality of the scheme.

数据完整性是可靠的云存储中的核心安全问题，已受到广泛关注。数据审核协议使验证者可以有效地检查外包数据的完整性，而无需下载数据。与现有数据审核协议的设计相关的主要研究挑战是审核过程的效率。由于现有协议倾向于审核所有云数据，因此实际上某些数据可能刚刚使用或即将使用，因此审核所有这些数据均无效，这是资源的浪费。在本文中，我们尝试通过尽我们所知引入用户行为预测算法来解决由于云数据完整性检查中的无效审核而造成的资源浪费。进一步来说，在有效审计的基础上，我们在数据完整性验证中引入了有效审计的概念。我们为这个新概念形式化了系统模型和安全模型。然后，通过使用用户行为预测算法，我们给出了提高审计效率并减少无效审计的方法。最后，我们开发协议的原型实现以验证该方案的实用性。