A Framework for Privacy-Preserving Multi-Party Skyline Query Based on Homomorphic Encryption
A Framework for Privacy-Preserving Multi-Party Skyline Query Based on Homomorphic Encryption
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DOI：
10.1109/access.2019.2954156
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发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Mahboob Qaosar;Kazi Md. Rokibul Alam;Asif Zaman;Chen Li;Saleh Ahmed;Md. Anisuzzaman Siddique;Y. Morimoto
中科院分区：
文献类型：
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作者：
Mahboob Qaosar;Kazi Md. Rokibul Alam;Asif Zaman;Chen Li;Saleh Ahmed;Md. Anisuzzaman Siddique;Y. Morimoto
如今，“大数据”的管理和分析对于世界各地的许多组织来说都是必不可少的。在许多情况下，多个组织希望在其组合数据库上执行数据分析。天际线查询是从大型数据库中选择代表性对象的流行操作之一，其中数据库中的任何其他对象都不占主导地位，称为“天际线”。与其他数据分析操作一样，多方天际线查询可以通过从参与组织的组合数据库中检索天际线对象来为参与组织提供益处。这种多方skyline查询要求在计算期间向其他方公开各个方的对象。不过，基于现今资讯科技时代对资料私隐及保安的关注，有关方面严禁披露个别人士的资料库。考虑到这个问题，我们提出了一个新的框架，隐私保护多方天际线查询，利用加性同态加密沿着与数据匿名化，扰动和随机化技术。我们提出的框架内的底层协议，确保每一个参与方可以识别其多方天际线对象，而不会透露的对象给他人在多方天际线查询。详细的隐私和安全性分析表明，该框架可以达到预期的计算目标，没有隐私泄漏。此外，通过复杂性分析，广泛的模拟和综合比较的性能评估也证明了所提出的框架的实用性和效率。
Nowadays, the management and analyses of ‘big data’ are becoming indispensable for numerous organizations all over the world. In many cases, multiple organizations want to perform data analyses on their combined databases. Skyline query is one of the popular operations for selecting representative objects from a large database, where any other object within the database does not dominate each of the representative objects, called ‘skyline’. Like other data analytics operations, the multi-party skyline query can provide benefits to the participating organizations by retrieving the skyline objects from their combined databases. Such a multi-party skyline query demands the disclosure of individual parties’ objects to others during the computation. But, owing to the data privacy and security concern of the present IT era, such disclosure of the individual parties’ databases is strictly prohibited. Considering this issue, we are proposing a new framework for the privacy-preserving multi-party skyline query, exploiting additive homomorphic encryption along with data anonymization, perturbation, and randomization techniques. The underlying protocols within our proposed framework ensure that every participating party can identify its multi-party skyline objects without revealing the objects to others during the multi-party skyline query. The detailed privacy and security analyses show that the proposed framework can achieve the desired computation goal without privacy leakage. Besides, the performance evaluation through complexity analyses, extensive simulations, and comprehensive comparison also demonstrate the utility and the efficiency of the proposed framework.