Privacy-Preserving Multi-Party Clustering: An Empirical Study
Privacy-Preserving Multi-Party Clustering: An Empirical Study
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DOI：
10.1109/cloud.2017.49
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
A. Silva;G. Bellala
中科院分区：
文献类型：
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作者：
A. Silva;G. Bellala
企业正在向数据驱动的业务流程过渡。在许多情况下，如果可以同时保护数据中描述的个人和组织的隐私和安全，多方希望共享数据以实现共同目标。针对日益增长的数据隐私需求，本文首次对隐私保护多方计算进行了全面的评估。作为一个案例研究，我们考虑聚类任务，其中包括分组的一组点的基础上，他们的相似性。我们的目标是了解当不同的参与方希望在保护其数据隐私的同时协作执行计算时所涉及的权衡。特别是，我们研究了集中式和分布式隐私保护解决方案（如加密和数据扰动）对聚类质量，隐私和计算性能的影响。我们的研究结果为服务提供商和用户提供了多方计算的新视角，突出了这些方法的缺点，并为未来的研究提供了机会。
Enterprises are transitioning towards data-driven business processes. There are numerous situations where multiple parties would like to share data towards a common goal, if it were possible to simultaneously protect the privacy and security of the individuals and organizations described in the data. Motivated by the increasing demands for data privacy, this paper provides the first comprehensive evaluation of privacy-preserving multi-party computation. As a case study, we consider the clustering task, which consists of grouping a set of points based on their similarity. Our goal is to understand the trade-offs involved when different parties want to collaboratively perform a computation while preserving their data privacy. In particular, we study the implications of centralized and distributed privacy-preserving solutions (such as encryption and data perturbation) on clustering quality, privacy and computational performance. Our results offer a new perspective on multi-party computation for both service providers and users, highlighting the drawbacks of these approaches and opening opportunities for future research.