Reference number : GC-2017-Q1-01669, amendment number : , agreement type : G, recipient legal name : Lu, Rongxing (University of New Brunswick)|Lu, Rongxing (Université du Nouveau-Brunswick), recipient country : CA, recipient province : nan, recipient city : New Brunswick|Nouveau-Brunswick, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Big data has been widely regarded as a new &ldquo;natural resource&rdquo; and has become a key driver of economic growth and competitiveness in Canada. In order to adapt Canada to the big data era, we need to prepare not only a new generation of data engineers skilled at managing big data, but also reliable big data process platforms to enable secure and privacy-preserving collaborative computing in big data era. The broad objective of the proposed research is therefore to focus on efficient, secure, and privacy-preserving big data processing and collaborative computing. 
As big data is characterized by high volume, velocity, and variety (&ldquo;3V&rdquo;), traditional security and privacy solutions cannot be directly migrated to fully address newly-emerging challenges in the context of big data. Therefore, we need to pay significant attention to big data security and privacy research. However, there are currently few works dedicated to security and privacy challenges in big data. To fill this gap, the efforts in this proposal shall shed new light on big data security and privacy research. Specifically, the proposed research will investigate a set of advanced security and privacy technologies using an interdisciplinary approach, i.e., by combining cryptography, distributed computing, and data mining techniques, to provide secure and reliable data collection, storage, transmission, and processing for big data. In particular, this proposal will address significant technical challenges arising from &ldquo;3V&rdquo; characteristics of big data, in the following four thrusts: 
i) developing lightweight and secure cryptographic protocols for big data in the phase of big data acquisition, organization, and analysis; 
ii) developing secure distributed programming frameworks for computing in big data; 
iii) developing privacy-preserving big data mining to prevent inadvertent privacy disclosures in big data; and 
iv) developing reliable, provenance-integrated, and access-controllable data provenance for big data. 
The proposed research will draw intensively and extensively on the Principal Investigator&rsquo;s research expertise, as well as strong supports from the CIC (Canadian Institute for Cybersecurity) affiliated with the University of New Brunswick Faculty of Computer Science. This cutting-edge research will generate new ideas and knowledge for the evolution of secure big data, enable HQP training and provide new secure big data solutions for Canada. The HQP trained through the program will be equipped with big data and security technologies, as well as wireless communications and mobile computing; these are crucial for success in the era of the Internet of Things and big data computing, and will help to ensure a prosperous future for the Canadian IT industry., program purpose french : Big data has been widely regarded as a new &ldquo;natural resource&rdquo; and has become a key driver of economic growth and competitiveness in Canada. In order to adapt Canada to the big data era, we need to prepare not only a new generation of data engineers skilled at managing big data, but also reliable big data process platforms to enable secure and privacy-preserving collaborative computing in big data era. The broad objective of the proposed research is therefore to focus on efficient, secure, and privacy-preserving big data processing and collaborative computing. 
As big data is characterized by high volume, velocity, and variety (&ldquo;3V&rdquo;), traditional security and privacy solutions cannot be directly migrated to fully address newly-emerging challenges in the context of big data. Therefore, we need to pay significant attention to big data security and privacy research. However, there are currently few works dedicated to security and privacy challenges in big data. To fill this gap, the efforts in this proposal shall shed new light on big data security and privacy research. Specifically, the proposed research will investigate a set of advanced security and privacy technologies using an interdisciplinary approach, i.e., by combining cryptography, distributed computing, and data mining techniques, to provide secure and reliable data collection, storage, transmission, and processing for big data. In particular, this proposal will address significant technical challenges arising from &ldquo;3V&rdquo; characteristics of big data, in the following four thrusts: 
i) developing lightweight and secure cryptographic protocols for big data in the phase of big data acquisition, organization, and analysis; 
ii) developing secure distributed programming frameworks for computing in big data; 
iii) developing privacy-preserving big data mining to prevent inadvertent privacy disclosures in big data; and 
iv) developing reliable, provenance-integrated, and access-controllable data provenance for big data. 
The proposed research will draw intensively and extensively on the Principal Investigator&rsquo;s research expertise, as well as strong supports from the CIC (Canadian Institute for Cybersecurity) affiliated with the University of New Brunswick Faculty of Computer Science. This cutting-edge research will generate new ideas and knowledge for the evolution of secure big data, enable HQP training and provide new secure big data solutions for Canada. The HQP trained through the program will be equipped with big data and security technologies, as well as wireless communications and mobile computing; these are crucial for success in the era of the Internet of Things and big data computing, and will help to ensure a prosperous future for the Canadian IT industry., agreement title english : Advanced Security and Privacy Technologies for Data Protection: Analysis, Design and Application in Big Data Era, agreement title french : Advanced Security and Privacy Technologies for Data Protection: Analysis, Design and Application in Big Data Era, agreement number : RGPIN, agreement value : 100000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada