The Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity: how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records ($AR$ARs) into the dataset, from which it constructs a set of artificial outliers ($AO$AOs) and artificial non-outliers ($ANO$ANOs). The $AO$AOs and $ANO$ANOs are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct $AR$ARs so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify $ANO$ANOs and $AO$AOs without executing mining. Furthermore, we build a strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight.

中文翻译：

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外包数据库的经过身份验证的异常值挖掘

数据挖掘即服务 (DMaS) 范式正成为研究的焦点，因为它允许缺乏专业知识和/或计算资源的数据所有者（客户）将他们的数据和挖掘需求外包给第三方服务提供者（服务器）。然而，外包带来了一些问题结果完整性: 客户端如何验证服务端返回的挖矿结果是否完整？在本文中，我们专注于异常值挖掘，这是一项重要的挖掘任务。以前的验证技术使用经过验证的数据结构 (ADS) 进行正确性验证，这可能会导致大量空间和通信成本。在本文中，我们提出了一种新颖的解决方案，该解决方案以更便宜的验证成本返回概率结果完整性保证。关键思想是插入一组人工记录（$AR$一种电阻s）进入数据集，从中构建一组人工异常值（$AO$一种哦s) 和人工非异常值 ($ANO$一种N哦s)。这$AO$一种哦沙 $ANO$一种N哦客户端使用 s 以概率保证检测任何不完整和/或不正确的挖掘结果。我们要解决的主要挑战是如何构建$AR$一种电阻s 以便他们不会改变原始记录的（非）异常值，同时保证客户可以识别 $ANO$一种N哦沙 $AO$一种哦s 不执行挖掘。此外，我们构建了一个策略博弈并表明只有当服务器返回正确的异常值时才存在纳什均衡。我们的实施和实验表明，我们的验证解决方案高效且轻量级。