隐私保护数据采掘（Privacy Preserving Data Mining，PPDM）解决了在不获取单个数据记录中精确信息的情况下，对聚集数据建立精确模型的问题。广泛研究的\emph{perturbation-based PPDM}方法将随机扰动引入个体值以在数据发布之前保护隐私。这种方法的以前的解决方案是有限的，在他们的默认假设的单级信任的数据挖掘。在这项工作中，我们放松了这一假设，并扩大了基于扰动的PPDM的范围多级信任（MLT-PPDM）。在我们的设置中，数据挖掘机越受信任，它可以访问的数据副本就越少。在这种设置下，恶意数据挖掘器可以通过各种手段访问相同数据的不同扰动副本，并且可以组合这些不同的副本以联合推断关于数据所有者不打算发布的原始数据的附加信息。防止这种\emph{diversity attacks}是提供MLT-PPDM服务的关键挑战。我们解决这一挑战，适当地相关的扰动在不同的信任级别的副本。我们证明，我们的解决方案是强大的多样性攻击方面，我们的隐私目标。也就是说，对于可以访问任意扰动副本集合的数据挖掘者来说，我们的解决方案可以防止他们联合重建原始数据，这比使用集合中任何单个副本的最佳努力更准确。我们的解决方案允许数据所有者按需为其数据生成任意信任级别的扰动副本。此功能为数据所有者提供了最大的灵活性。
英文摘要：
Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied \emph{perturbation-based PPDM} approach introduces random perturbation to individual values to preserve privacy before data is published. Previous solutions of this approach are limited in their tacit assumption of single-level trust on data miners.
In this work, we relax this assumption and expand the scope of perturbation-based PPDM to Multi-Level Trust (MLT-PPDM). In our setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine these diverse copies to jointly infer additional information about the original data that the data owner does not intend to release. Preventing such \emph{diversity attacks} is the key challenge of providing MLT-PPDM services. We address this challenge by properly correlating perturbation across copies at different trust levels. We prove that our solution is robust against diversity attacks with respect to our privacy goal. That is, for data miners who have access to an arbitrary collection of the perturbed copies, our solution prevent them from jointly reconstructing the original data more accurately than the best effort using any individual copy in the collection. Our solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on-demand. This feature offers data owners maximum flexibility.
Output Privacy Protection With Pattern-Based Heuristic Algorithm
隐私保护数据采掘（Privacy Preserving Data Mining，PPDM）是一个正在进行的研究领域，旨在弥合协同数据采掘和数据机密性之间的差距。这种方法保证了输出的隐私性，防止挖掘的模式（项集）的恶意推理问题。提出了一种基于模式的Maxcover算法，并给出了实验结果。该算法最大限度地减少了源数据库和已发布数据库之间的不相似性;此外，即使具有任意低的支持度阈值，对手或对手也无法从已发布数据库中检索到受保护的模式。
