Data Release - Trust, Identity, Privacy and Security

数据发布 - 信任、身份、隐私和安全

• 批准号: EP/N028139/1
• 负责人: Mark Jones
• 金额: $ 126.63万
• 依托单位: Swansea University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN028139%2F1
• 关键词: Data; Release; Trust; Identity; Privacy

The Open Data Initiative (ODI) demonstrates that there is a growing ambition from government to publish internal data as open data sets. (See https://data.gov.uk). Data custodians, particularly large governmental organisations such as the DVLA and HMRC, have a legal duty, enforced by the Information Commissioner's Office (ICO), and social duty of care to the public, to ensure that privacy is not breached by the release of data as open data sets. These large organisations face an increasingly difficult task in establishing whether the release of data will result in enough data being open to triangulate individuals and destroy privacy.Our research takes a collaborative approach uniquely combining formal methods, counter-fraud, data mining and data visualization to produce new tools, methodologies and theory for working with data release. We will produce tools that allow interactive analysis of data sets to determine if data release can combine with existing data to triangulate personal data. We will take a new approach of using formal notions about data, memorandums of understanding (of data use) and criteria of control to auto-generate software tools that allow the user to manipulate, investigate and analyse data sets for potential unintended consequences if released.We will undertake empirical studies with data keepers, data users and members of the public to inform data policies surrounding release of data and integrate this within our toolsets and methodologies.

开放数据计划（ODI）表明，政府将内部数据作为开放数据集发表越来越大。 （请参阅https://data.gov.uk）。数据保管人，尤其是大型政府组织，例如DVLA和HMRC，由信息专员办公室（ICO）执行的法律义务，以及向公众提供照顾的社会义务，以确保不会因开放数据集的数据发布而违反隐私。这些大型组织在确定数据发布是否会导致足够的数据开放以使个人开放并破坏隐私方面面临越来越困难的任务。我们的研究采用一种协作方法独特地结合了形式方法，反欺诈，数据挖掘，数据挖掘和数据可视化以产生新的工具，方法，方法论和理论来使用数据释放。我们将生成允许对数据集进行交互式分析的工具，以确定数据发布是否可以与现有数据结合以三角剖分个人数据。我们将采用一种新的方法，即使用有关数据的正式概念，理解备忘录（数据使用）和控制标准自动生成的软件工具，这些工具使用户可以通过释放，对潜在的意外后果进行操作，调查和分析数据集，以确保我们的潜在意外后果。如果我们将对数据进行了数据范围，数据用户和数据策略的信息，以释放数据和数据范围，以释放数据和整合。

项目成果

A DEEP CONVOLUTIONAL AUTO-ENCODER WITH EMBEDDED CLUSTERING

• DOI: 10.1109/icip.2018.8451506
• 发表时间: 2018-01-01
• 期刊: 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
• 作者: Alqahtani, A.;Xie, X.;Jones, M. W.
• 通讯作者: Jones, M. W.

Realising Intensional S4 and GL Modalities

实现内涵 S4 和 GL 模式

• DOI: 10.4230/lipics.csl.2022.14
• 发表时间: 2022
• 期刊: Leibniz International Proceedings in Informatics, LIPIcs
• 作者: Chen L.-T.

TimeCluster: dimension reduction applied to temporal data for visual analytics

• DOI: 10.1007/s00371-019-01673-y
• 发表时间: 2019-06-01
• 期刊: VISUAL COMPUTER
• 影响因子: 3.5
• 作者: Ali, Mohammed;Jones, Mark W.;Williams, Mark
• 通讯作者: Williams, Mark

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

• DOI: 10.1109/access.2019.2958551
• 发表时间: 2019-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Ali, Mohammed;Alqahtani, Ali;Xie, Xianghua
• 通讯作者: Xie, Xianghua

Recent Trends in Algebraic Development Techniques - 24th IFIP WG 1.3 International Workshop, WADT 2018, Egham, UK, July 2-5, 2018, Revised Selected Papers

代数发展技术的最新趋势 - 第 24 届 IFIP WG 1.3 国际研讨会，WADT 2018，英国埃格姆，2018 年 7 月 2-5 日，修订后的精选论文

• DOI: 10.1007/978-3-030-23220-7_3
• 发表时间: 2019
• 作者: Chen L