Privacy in Context

隐私是关于选择和环境。选择是至关重要的，因为每个人都必须能够定义他们想要保留的隐私。上下文定义了私有信息共享的框架，以及共享后对其使用的限制。每个人都会对什么是隐私做出不同的选择，并且会有多种应该共享隐私信息的环境。隐私保护和感知数据系统需要尊重个人选择，并允许用户定义由于不同上下文而产生的差异。数据隐私必须以类似的方式定义。任何收集到的关于我们的数据都应该按照个人对数据的偏好（或者理想情况下，更一般地说，关于收集数据的对象）来处理。这些首选项定义了提供者对数据项敏感性的选择，以及何时和与谁共享数据（上下文）。******目前的隐私保护方法要么对收集的任何数据施加通用的隐私结构，要么至多提供一个选择加入或选择退出的参与模式。我们的方法认为，只有在用户允许作为个人选择的情况下，数据才应该包含在分析活动中。我们正在调查与数据管理相关的连接上下文和选择相关的端到端挑战。我们确定了三个主要的新颖贡献：首先，我们将开发一个隐私承诺模型，该模型捕获数据隐私的关键方面，包括隐私元数据；我们将开发一个新的隐私本体，明确捕获个人偏好，允许基于上下文的不同用途。其次，我们将开发技术，有效地将收集的数据附加到基于知情同意的隐私承诺上。数据和隐私承诺必须不可撤销地附加。这将需要新的数据处理范例和数据结构，能够保持这种紧密联系，同时允许有效的数据处理和分析。一个成功的策略也将促进更好的分析，因为增加了数据完整性，从而增加了效用。第三，我们将开发和/或调整系统，有效地处理、存储和交流数据，并以一种永远不会破坏联系的方式，从而保证在过程的每一步都遵守隐私承诺。我们将研究一些不同的数据收集范例来测试我们的隐私模型，包括传感器系统(例如。物联网)、网页浏览活动、销售点系统、智能公用事业以及市场分析系统等“大数据”系统。******加拿大人非常重视隐私，因为它是现代民主的基石。在过去20年里，隐私保护的重要性逐渐下降，但现在它被视为一个优先级高得多的问题。我的工作将开发保护这种价值的系统，并通过允许个人控制他们应该作为私人持有的内容来实现。

Privacy is about choice and context. Choice is critical because each individual must be able to define what they want to hold as private. Context defines the framework around which private information is shared and the limits on its use once it has been shared. Each individual will make different choices about what is private and will have multiple contexts in which the private information should be shared. Privacy preserving and aware data systems are needed that respect individual choice and allow the user to define variance arising as a result of different contexts. Data privacy must be defined analogously. Any data collected about us should be treated in conformance with individual preferences about the data (or ideally, and more generally, about whom the data is collected.) These preferences define the provider's choices about a data item's sensitivity, and when and with whom the data can be shared (context).******Current privacy approaches either impose a generic privacy structure on any data collected and at best provide only an opt-in or opt-out participation model. Our approach argues that data should only be included in analyses activities if the user permits it as a personal choice. We are investigating end-to-end challenges associated with connecting context and choice with respect to data management. We identify three major novel contributions: First, we will develop a privacy commitment model that captures key aspects of data privacy including privacy meta-data and we will develop a new privacy ontology that explicitly captures individual preferences that allow for different uses based on context. Secondly, we will develop techniques that efficiently attach collected data to privacy commitments based on informed consent. The data and privacy commitments must be irrevocably attached. This will require new data processing paradigms and data structures capable of maintaining this tight linkage while permitting efficient data processing and analytics. A successful strategy will also facilitate better analytics because of increased data integrity thereby increasing utility. Thirdly, we will develop and/or adapt systems that process, store, and communicate data efficiently and in such a way that the linkage is never broken thereby guaranteeing the privacy commitments are honoured at each step of the process. We will investigate a number of different data collection paradigms to test our privacy model including sensor system (eg. IoT), web browsing activities, point-of-sales systems, smart utilities, and “big data” systems such as market analysis systems.******Canadians highly value privacy as it is the cornerstone of modern democracies. Privacy protection has slowly degraded over the past two decades but its importance is now seen as a much higher priority issue. My work will develop systems that protect this value and do so by allowing individuals to control what about them should be held as private.