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Grant: Strategic Projects - Group

Dalhousie University

$109,505.00

Novel Techniques for Data Privacy Protection

EF 2013-14

Résumé
Data privacy is an important and far-reaching concept of the modern information technology. Due to its progressive legislation, the high social appreciation of privacy as a social value, the presence of high-calibre Privacy Commissionaires, as well as the leadership of companies such as Privacy Analytics, Canada is perceived as one of the world leaders in data privacy. This project will strengthen and further enhance this leadership position, by presenting new research solutions, by assisting the industry partner in offering new products, and by training a number of students in the techniques of the data privacy. Specifically, we propose to tackle - together with our industrial partner - several interconnected tasks and find solutions that address real privacy concerns which are not met today. For instance, we propose to work on privacy models and techniques that will detect human error and inadvertent privacy leaks in written documents. Another text-centered task related to privacy in the medical communication setting is differentiation between self-disclosed medical information, and such information published by others. We will develop an automated text analysis tool capable of such differentiation. We also propose a novel approach which will enable users of popular social networks to better control who knows what about them. Finally, we propose to work on a model of data privacy that will be used by practitioners (e.g. data curators) to measure the degree of privacy ensured by applying a given privacy protection technique on a given data, as well as the cost of an attack on the protection applied. The common denominator for much of the proposed research is the use of Machine Learning techniques, in which our team has advanced expertise and international leadership. We anchor our research in the areas of importance to Canada's health care sector, where our industrial partner is situated. The solutions we provide will be adopted by Canadian industry to supply better privacy solution in Canada and globally.
Programme
Strategic Projects - Group
Bénéficiaire
Emplacement
Nova Scotia, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
413128-2011
Name nom
Matwin, Stan
Department département
Computer Science, Faculty of
Organization id
79
Institution établissement
Dalhousie University
Province en
Nova Scotia
Province fr
Nouvelle-Écosse
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2013
Competition year année de concours
2011
Award amount
109505
Program id
STPGP
Program name en
Strategic Projects - Group
Program name fr
Projets stratégiques - De groupe
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1567
Committee name en
Strategic Projects Selection Panel - ICT
Committee name fr
Comité de sélection des projets stratégiques - ICT
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2700
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information technology
Research subject fr
Technologie de l'information
Partie
3
Nb partie
3
Application title
Novel Techniques for Data Privacy Protection
Keyword
NSERC
Application summary
Data privacy is an important and far-reaching concept of the modern information technology. Due to its progressive legislation, the high social appreciation of privacy as a social value, the presence of high-calibre Privacy Commissionaires, as well as the leadership of companies such as Privacy Analytics, Canada is perceived as one of the world leaders in data privacy. This project will strengthen and further enhance this leadership position, by presenting new research solutions, by assisting the industry partner in offering new products, and by training a number of students in the techniques of the data privacy. Specifically, we propose to tackle - together with our industrial partner - several interconnected tasks and find solutions that address real privacy concerns which are not met today. For instance, we propose to work on privacy models and techniques that will detect human error and inadvertent privacy leaks in written documents. Another text-centered task related to privacy in the medical communication setting is differentiation between self-disclosed medical information, and such information published by others. We will develop an automated text analysis tool capable of such differentiation. We also propose a novel approach which will enable users of popular social networks to better control who knows what about them. Finally, we propose to work on a model of data privacy that will be used by practitioners (e.g. data curators) to measure the degree of privacy ensured by applying a given privacy protection technique on a given data, as well as the cost of an attack on the protection applied. The common denominator for much of the proposed research is the use of Machine Learning techniques, in which our team has advanced expertise and international leadership. We anchor our research in the areas of importance to Canada's health care sector, where our industrial partner is situated. The solutions we provide will be adopted by Canadian industry to supply better privacy solution in Canada and globally.