Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

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.