...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Future generation computer systems >Answering differentially private queries for continual datasets release
【24h】

Answering differentially private queries for continual datasets release

机译：回答差异私有查询以获取连续数据集

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

Privacy preserving data release is a hot topic that attracts a lot of attentions in data mining, machine learning, and social network communities. Most studies on privacy preserving focus on static data releases; however, data are usually updated periodically. As a potential solution, differential privacy addresses continual data release by simplifying it into an event stream release problem. This approach overlooks the relationship between events, which is defined as coupled information in this paper. We argue that datasets cannot be simplified as an event stream due to the coupled information. In addition, the coupled information may reveal more private information than expected. This work proposes a privacy-preserving mechanism that explicitly identify the coupled information in continually released datasets. In stead of simplifying datasets to event streams, this mechanism considers the continual released datasets as coupled datasets based on the relationship between the same individual in different datasets, and the relationship between different individuals in the same dataset. We also propose the notion of coupled sensitivity for answering differentially private queries and develop an iterative based coupled continual release algorithm, called CCR, that answers these queries with a large set of differentially private results. Theoretical analysis proves the privacy of this method, and an extensive performance study shows that CCR outperforms traditional differential privacy mechanisms when answering a large set of queries. (C) 2017 Elsevier B.V. All rights reserved.
机译：隐私保护数据发布是一个热门话题，在数据挖掘，机器学习和社交网络社区中引起了很多关注。大多数关于隐私保护的研究都集中在静态数据发布上。但是，数据通常会定期更新。作为一种潜在的解决方案，差分隐私通过将数据简化为事件流发布问题来解决连续数据发布问题。这种方法忽略了事件之间的关系，在本文中将其定义为耦合信息。我们认为，由于耦合的信息，数据集不能简化为事件流。此外，耦合的信息可能会显示比预期更多的私人信息。这项工作提出了一种隐私保护机制，可以在连续发布的数据集中明确标识耦合信息。代替将数据集简化为事件流，此机制基于不同数据集中同一个人之间的关系以及同一数据集中不同个人之间的关系，将连续发布的数据集视为耦合数据集。我们还提出了用于回答差分私有查询的耦合敏感度的概念，并开发了基于迭代的耦合连续释放算法，称为CCR，该算法以大量差分私有结果来回答这些查询。理论分析证明了这种方法的私密性，一项广泛的性能研究表明，在回答大量查询时，CCR优于传统的差异性隐私机制。 （C）2017 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Future generation computer systems》 |2018年第10期|816-827|共12页
  • 作者

    Zhu Tianqing; Li Gang; Xiong Ping; Zhou Wanlei;

  • 作者单位

    Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Hubei, Peoples R China|Deakin Univ, Sch Informat Technol, 221 Burwood Highway, Melbourne, Vic 3125, Australia;

    Deakin Univ, Sch Informat Technol, 221 Burwood Highway, Melbourne, Vic 3125, Australia;

    Zhongnan Univ Econ & Law, Sch Informat & Secur Engn, Wuhan, Hubei, Peoples R China;

    Deakin Univ, Sch Informat Technol, 221 Burwood Highway, Melbourne, Vic 3125, Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Privacy preserving; Continual data release; Differential privacy;

    机译：隐私保护;连续数据发布;差异性隐私;

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