Secure and flexible cloud-assisted association rule mining over horizontally partitioned databases
Secure and flexible cloud-assisted association rule mining over horizontally partitioned databases
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DOI：
10.1016/j.jcss.2016.12.005
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Cheng Huang;Rongxing Lu;K. Choo
中科院分区：
文献类型：
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作者：
Cheng Huang;Rongxing Lu;K. Choo
随着大数据和云计算的最新趋势，数据挖掘也吸引了相当大的兴趣，因为它有潜力处理云中的分布式数据。然而，现有的数据挖掘技术可能不会直接部署，因为我们需要避免在挖掘来自不同来源的数据时意外泄露隐私。在本文中，我们提出了一个安全和灵活的云辅助关联规则挖掘水平分区数据库。使用所提出的方案，数据所有者可以提供他们的数据，并在云中灵活地挖掘关联规则，同时确保隐私泄露的风险最小。然后，我们表明，我们提出的方案实现了隐私保护挖掘的关联规则，并提供抵御共谋攻击的弹性。一个比较的总结表明，该计划是更有效的，在计算成本方面，相对于现有的几个同态加密为基础的计划。
With recent trends in big data and cloud computing, data mining has also attracted considerable interest due to its potential to deal with distributed data in the cloud. However, existing data mining technologies may not be directly deployed as we need to avoid accidental privacy disclosure when data from different sources are mined. In this paper, we propose a secure and flexible cloud-assisted association rule mining over horizontally partitioned databases. Using the proposed scheme, data owners can provide their data and mine the association rules in the cloud flexibly, while being assured of minimal risks of privacy leakage. We then show that our proposed scheme achieves privacy-preserving mining of association rules, and provides resilience against collusion attacks. A comparative summary demonstrates that the proposed scheme is more efficient, in terms of computational costs, relative to several existing homomorphic-encryption-based schemes.