NeTS: Small: Protecting Privacy While Providing Utility in Published Network Mobility Traces Using Differential Privacy
NeTS: Small: Protecting Privacy While Providing Utility in Published Network Mobility Traces Using Differential Privacy
批准号:
1421325
负责人:
Gerome Miklau
金额:
$47.87万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-10-01 至 2018-09-30
中文摘要
个人在WiFi网络中的接入点之间或蜂窝网络中的蜂窝塔之间的移动的记录信息通常被视为私人数据,并且不被网络运营商泄露。 然而,这种真实世界个人移动性的痕迹对于研究人员和工程师评估新的移动的网络协议、架构和应用至关重要。 该项目将开发和评估用于操纵然后发布移动轨迹的技术,以便(i)向原始轨迹中包含其数据的用户提供定义明确,正式证明的隐私保证,以及(ii)当用于常见的计算机网络和协议设计和分析任务时,发布的轨迹提供高准确性。 该项目中开发的差分隐私技术使用原始数据的受约束的前缀表示来确定原始数据的底层表示,并明智地分配满足差分隐私(DP)约束所需的随机噪声。 衍生DP轨迹的效用-使用衍生DP轨迹实现的任务特定性能与使用原始轨迹实现的任务特定性能之间的差异-将针对四个常见和代表性用例进行评估。原始数据本身将从运行的4,500节点WiFi校园无线网络中获得。由于更受限制的输出DP数据可以为给定隐私预算的特定任务提供更高的效用(比DP跟踪),因此该项目还将检查特定任务的专用DP输出的效用权衡。 该项目还将开发已发布的DP数据的误差范围,为网络分析师提供与DP数据相关的“随机性”程度的定量测量。该项目允许“真实的世界”的网络移动数据提供给网络研究人员和分析师,同时为用户提供正式的隐私保证,其活动在该数据中表示。 作为该项目的一部分,公开提供的用户移动性的特定DP痕迹也将是有趣和有用的,随后围绕最合适和有用的私人移动数据形式的社区讨论也将如此。更广泛地说,在“大数据”的新生时代,隐私变得至关重要。移动的无线用户和他们在网络接入点之间的轨迹只是轨迹足迹的一个示例-访问的物理位置、遇到的熟人、访问的网站或无数其他活动中的任何一个的序列是远远超出联网研究社区的重要兴趣的“轨迹”。因此,在本项目中进行的特定领域的研究可能会在这些领域和许多其他领域中得到应用。指导研究生研究助理和本科REU学生的多样化群体,并将隐私相关的研究成果纳入研究生级别的研讨会和(更广泛地)隐私问题纳入本科网络课程,将为本项目的研究提供更广泛的影响。
英文摘要
Recorded information of an individual's movement among access points in a WiFi network or among cell towers in a cellular network is typically regarded as private data and not divulged by network operators. Yet such traces of real-world individual mobility are critically important for researchers and engineers in evaluating new mobile network protocols, architectures, and applications. This project will develop and evaluate techniques for manipulating and then publishing mobility traces in a manner such that (i) well-defined, formally-proven, privacy guarantees are provided to the users whose data are contained in the original traces, and (ii) the published traces provide high accuracy when used in common computer network and protocol design and analysis tasks. The differential privacy techniques developed in this project use a constrained trajectory-prefix representation of the original data to determine the underlying representation of the original data and judiciously allocate random noise needed to satisfy differential privacy (DP) constraints. The utility of the derived DP trajectories - the difference between task-specific performance realized using the derived DP trajectories and realized using the original trajectories - will be evaluated for four common and representative use cases. The original data itself will be obtained from an operational 4,500-node WiFi campus wireless network. Since a more restricted output DP data may provide higher utility (than DP traces) for a specific task for a given privacy budget, this project will also examine utility tradeoffs from specializing DP output for specific tasks. The project will also develop error bounds on published DP data to provide the network analyst with a quantitative measure of the degree of "randomness" associated with the DP data. This project allows 'real world' network mobility data to be made available to networking researchers and analysts, while at the same time providing formal privacy guarantees to users whose activities are represented in that data. The specific DP traces of user mobility made publicly available as part of this project will also be of interest and use, as will the ensuing community discussion around the most appropriate and useful forms of private mobility data. More broadly, in the nascent era of "big data," privacy has become of paramount importance. Mobile wireless users and their trajectories among network access points are just one example of trajectory footprints - sequences of physical locations visited, acquaintances met, web sites visited or any of a myriad of other activities are 'trajectories' of significant interest well beyond the networking research community. The domain-specific research undertaken in this project may thus find application in these and many other areas. The mentoring of a diverse group of graduate research assistants and undergraduate REU students and incorporation of privacy-related research results into graduate-level seminars and (more broadly) privacy issues into undergraduate network curriculum will provide additional broader impact for this project's research.
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会议论文
SATC: CORE: Medium: Principles and Algorithms for Visual Data Exploration Under Differential Privacy
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批准号:1954814
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项目类别:Standard Grant
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资助金额:$119.11万
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依托单位:
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批准号:1741254
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依托单位:
TWC: Medium: Collaborative: Re[DP]: Realistic Data Mining Under Differential Privacy
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批准号:1409143
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项目类别:Standard Grant
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资助金额:$45.84万
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TC:Large:Collaborative Research:Practical Privacy: Metrics and Methods for Protecting Record-level and Relational Data
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批准号:1012748
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资助金额:$56.99万
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负责人:Gerome Miklau
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依托单位:
CAREER: Securing history: privacy and accountability in database systems
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批准号:0643681
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资助金额:$50.0万
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负责人:Gerome Miklau
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依托单位:
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项目类别:Continuing Grant
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财政年份:2006
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负责人:Gerome Miklau
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依托单位:
国内基金
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