Abstract Due to the rising costs of maintaining IT infrastructures for large-scale data mining, it is becoming a trend for data owners to outsource data mining tasks together with storage to cloud service providers, however, which also arouses security concerns on unauthorized breaches of data confidentiality and result integrity. Existing solutions yet seldom protect data privacy whilst guaranteeing result integrity. To address these issues, this paper proposes a series of privacy-preserving building blocks by employing Shamir’s secret sharing scheme. Based on those subprotocols, an efficient frequent itemset mining protocol is designed under hybrid cloud setting, in which the public unreliable cloud and semi-trusted cloud cooperate to mine frequent patterns over the encrypted database. Our scheme not only protects the privacy of datasets from frequency analysis attack, but also verifies the integrity of mining results. Theoretical analysis demonstrates that the scheme ensures security as well as fault tolerance under our threat model. Experimental evaluations show that our proposed protocol outperforms the similar solution regarding efficiency while it can detect and correct cloud servers’ errors effectively.

中文翻译：

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在混合云环境中实现容错和安全的频繁项集挖掘外包

摘要 由于大规模数据挖掘的 IT 基础设施维护成本不断上升，数据所有者将数据挖掘任务连同存储一起外包给云服务提供商成为一种趋势，但这也引起了对未经授权的数据泄露的安全担忧。保密性和结果完整性。现有的解决方案很少能在保证结果完整性的同时保护数据隐私。为了解决这些问题，本文采用 Shamir 的秘密共享方案提出了一系列隐私保护构建块。基于这些子协议，在混合云环境下设计了一种高效的频繁项集挖掘协议，其中公共不可靠云和半可信云合作在加密数据库上挖掘频繁模式。我们的方案不仅可以保护数据集的隐私免受频率分析攻击，还可以验证挖掘结果的完整性。理论分析表明，该方案在我们的威胁模型下确保了安全性和容错性。实验评估表明，我们提出的协议在效率方面优于类似的解决方案，同时它可以有效地检测和纠正云服务器的错误。