Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources can leak such information. Data sharing (e.g. cross-agency data sharing) for machine learning and analytics is one of the important components in data science. However, due to privacy concerns, data should be enforced with strong privacy guarantees before sharing. Different privacy-preserving approaches were developed for privacy preserving data sharing; however, identifying the best privacy-preservation approach for the privacy-preservation of a certain dataset is still a challenge. Different parameters can influence the efficacy of the process, such as the characteristics of the input dataset, the strength of the privacy-preservation approach, and the expected level of utility of the resulting dataset (on the corresponding data mining application such as classification). This paper presents a framework named Privacy Preservation as a Service (PPaaS) to reduce this complexity. The proposed method employs selective privacy preservation via data perturbation and looks at different dynamics that can influence the quality of the privacy preservation of a dataset. PPaaS includes pools of data perturbation methods, and for each application and the input dataset, PPaaS selects the most suitable data perturbation approach after rigorous evaluation. It enhances the usability of privacy-preserving methods within its pool; it is a generic platform that can be used to sanitize big data in a granular, application-specific manner by employing a suitable combination of diverse privacy-preserving algorithms to provide a proper balance between privacy and utility.

个人身份信息（PII）可以通过各种渠道进入网络空间，并且许多潜在的来源都可能泄漏此类信息。用于机器学习和分析的数据共享（例如跨机构数据共享）是数据科学中的重要组成部分之一。但是，由于隐私方面的考虑，在共享数据之前，应在强有力的隐私保证下强制执行数据。针对隐私保护数据共享，开发了不同的隐私保护方法。但是，为某个数据集的隐私保护确定最佳的隐私保护方法仍然是一个挑战。不同的参数会影响过程的效率，例如输入数据集的特征，隐私保护方法的强度，以及所得数据集的预期效用水平（在相应的数据挖掘应用程序（例如分类）上）。本文提出了一个名为P rivacy P预留一个S ^一个 小号服务（PPaaS）来降低这种复杂性。所提出的方法通过数据扰动采用选择性隐私保护，并着眼于可以影响数据集隐私保护质量的不同动态。PPaaS包括数据扰动方法库，对于每个应用程序和输入数据集，PPaaS在经过严格评估后会选择最合适的数据扰动方法。它增强了其池中隐私保护方法的可用性；它是一个通用平台，可通过采用各种隐私保护算法的适当组合来在隐私和实用程序之间实现适当的平衡，从而以特定的，特定于应用程序的方式对大数据进行清理。