Grant: Alliance Grants
University of Waterloo
$75,000.00
Privacy-Preserving Graph Analytics Engine
FY 2022-23
Summary
Graph mining can bring great social and economic values but leak sensitive information about individuals. Though differential privacy has appeared as a strong privacy guarantee for private data analysis, existing systems and algorithms for differential privacy mainly support tabular data. Due to the complex and diverse information within graphs, extending existing privacy notions, algorithms, and systems for tabular data to graph data face many challenges. So far, there are no real-world graph database systems that can support the analysis of the private graphs. The primary objective of this research is to build a privacy-preserving graph analytics engine that can answer interactive queries on private graphs accurately and efficiently while ensuring a provable privacy guarantee. We intend to formalize a novel and customizable privacy framework for graph data, support interactive graph queries using state-of-the-art graph database systems, and enable graph analysis in a distributed setting.
Program
Alliance Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
551061-2020
Name nom
He, Xi X
Department département
Computer Science, David R. Cheriton School of
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2020
Award amount
75000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
2711
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Database management
Research subject fr
Gestion de bases de données
Installment
inst:3
Partie
1
Nb partie
3
Application title
Privacy-Preserving Graph Analytics Engine
Application summary
Graph mining can bring great social and economic values but leak sensitive information about individuals. Though differential privacy has appeared as a strong privacy guarantee for private data analysis, existing systems and algorithms for differential privacy mainly support tabular data. Due to the complex and diverse information within graphs, extending existing privacy notions, algorithms, and systems for tabular data to graph data face many challenges. So far, there are no real-world graph database systems that can support the analysis of the private graphs. The primary objective of this research is to build a privacy-preserving graph analytics engine that can answer interactive queries on private graphs accurately and efficiently while ensuring a provable privacy guarantee. We intend to formalize a novel and customizable privacy framework for graph data, support interactive graph queries using state-of-the-art graph database systems, and enable graph analysis in a distributed setting.
