• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on dependable and secure computing >Authenticated Outlier Mining for Outsourced Databases
【24h】

Authenticated Outlier Mining for Outsourced Databases

机译：经过身份验证的外部数据库的异常挖掘

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：数据挖掘的AS-Service（DMA）范式正在成为研究的重点，因为它允许缺乏专业知识和/或计算资源将其数据和采矿需求外包给第三方的数据所有者（客户）服务提供商（服务器）。但是，外包提出了关于结果完整性的一些问题：客户端如何验证服务器返回的挖掘结果都是声音和完整的吗？在本文中，我们专注于异常挖掘，这是一个重要的采矿任务。以前的验证技术使用经过身份验证的数据结构（广告）进行正确的身份验证，这可能会产生多种空间和通信成本。在本文中，我们提出了一种新的解决方案，返回概率的结果完整性保证，验证成本更便宜。关键的想法是将一组人工记录（$ AR $ ARS）插入数据集中，它从它构建一组人工异常值（$ AO $ AOS）和人工非异常值（$ ANO $ ANOS）。客户使用$ AO $ AOS和$ ANO $ ANO检测任何不完整和/或不正确的挖掘结果，具有概率的保证。我们地址的主要挑战是如何构建$ AR $ ARS，以便他们不会改变原始记录的（非）差异，同时保证客户可以在不执行挖掘的情况下识别$ ANO $ ANO和$ AO $ AOS。此外，我们建立一个战略游戏，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是高效和重量轻的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号