基于阈值动态调整的重复数据删除方案

云存储已经成为一种主流应用模式.随着用户及存储数据量的增加，云存储提供商采用重复数据删除技术来节省存储空间和资源.现有方案普遍采用统一的流行度阈值对所有数据进行删重处理，没有考虑到不同的数据信息具有不同的隐私程度这一实际问题.提出了一种基于阈值动态调整的重复数据删除方案，确保了上传数据及相关操作的安全性.提出了理想阈值的概念，消除了传统方案中为所有数据分配统一阈值所带来的弊端.使用项目反应理论确定不同数据的敏感性及其隐私分数，保证了数据隐私分数的适用性，解决了部分用户忽视隐私的问题.提出了基于数据加密的隐私分数查询反馈机制，在此基础上，设计了流行度阈值随数据上传的动态调整方法.实验数据及对比分析结果表明，基于阈值动态调整的重复数据删除方案具有良好的可扩展性和实用性.

Deduplication Scheme Based on Threshold Dynamic Adjustment

Cloud storage has become a major application model. As the number of users and data volume increase, cloud storage providers use deduplication technology to reserve storage space and resources. Existing solutions generally use a uniform popularity threshold to process all the data, while the issue is not addressed that different data information should have different privacy levels. A deduplication scheme is proposed based on threshold dynamic adjustment to ensure the security of uploaded data and related operations. The concept of ideal threshold is introduced, which can be used to eliminate the drawbacks of uniform threshold in the traditional schemes. The item response theory is adopted to determine the sensitivity of different data and their privacy scores, which ensures the applicability of data privacy scores, it can solve the problem that some users care little about privacy issues. A privacy score query and response mechanism are proposed based on data encryption. On this basis, the dynamic adjustment method of the popularity threshold is designed for data uploading. Experiment results and comparative analysis show that the proposed scheme based on threshold dynamic adjustment has sound scalability and solid practicability.