隐私感知数据清理即服务（扩展版）
Privacy-Aware Data Cleaning-as-a-Service (Extended Version)
摘要：
数据清理是企业普遍存在的问题，因为他们试图从数据中获得价值。网络和云计算技术的最新进展推动了称为数据库即服务的新计算范例，其中数据管理任务被外包给大型服务提供商。在本文中，我们考虑一种数据清理即服务模型，该模型允许客户端与托管精选数据和敏感数据的数据清理提供商进行交互。我们介绍PACAS：隐私感知数据清洗即服务模型，其促进具有对数据的客户端查询请求的各方之间的交互，以及使用根据数据敏感度计算价格的数据定价方案的服务提供者。我们提出了新的扩展模型来定义广义的数据修复，混淆敏感数据，允许客户端和服务提供商之间的数据共享。我们提出了一个新的语义距离度量来量化这种修复的效用，我们重新定义了广义值存在下的一致性概念。PACAS模型使用（X，Y，L）-匿名，扩展了现有的数据发布技术，以考虑数据中的语义，同时保护敏感值。我们对真实数据的评估表明，PACAS保障语义相关的敏感值，并提供较低的修复错误相比，现有的隐私意识的清洁技术。
英文摘要：
Data cleaning is a pervasive problem for organizations as they try to reap value from their data. Recent advances in networking and cloud computing technology have fueled a new computing paradigm called Database-as-a-Service, where data management tasks are outsourced to large service providers. In this paper, we consider a Data Cleaning-as-a-Service model that allows a client to interact with a data cleaning provider who hosts curated, and sensitive data. We present PACAS: a Privacy-Aware data Cleaning-As-a-Service model that facilitates interaction between the parties with client query requests for data, and a service provider using a data pricing scheme that computes prices according to data sensitivity. We propose new extensions to the model to define generalized data repairs that obfuscate sensitive data to allow data sharing between the client and service provider. We present a new semantic distance measure to quantify the utility of such repairs, and we re-define the notion of consistency in the presence of generalized values. The PACAS model uses (X,Y,L)-anonymity that extends existing data publishing techniques to consider the semantics in the data while protecting sensitive values. Our evaluation over real data show that PACAS safeguards semantically related sensitive values, and provides lower repair errors compared to existing privacy-aware cleaning techniques.
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