Scholar
Masoumeh Shafieinejad
Google Scholar ID: WLz3rp0AAAAJ
Researcher at Vector Institute
Citations & Impact
Citations
255
H-index
6
i10-index
6
Publications
13
Co-authors
17
list available
Publications
Resume (English only)
Academic Achievements
- No specific publications, awards, patents, or projects listed.
Research Experience
- Current researcher at Vector Institute, working on privacy-preserving federated learning and synthetic data generation for finance and health data; former research intern at National Research Council (NRC) in Canada, designing a secure mechanism to perform join operation over encrypted data; summer 2020 intern at Microsoft Research (MSR) in the US, focusing on privacy techniques (in particular differential privacy) for correlated data while training models on a graph of organizational communications (e.g., emails); previously worked as a senior cryptography consultant at Royal Bank of Canada, leading preparation for post-quantum cryptography migration, consulting with technology groups, and developing cryptographic architectural patterns and standards.
Education
- PhD from the University of Waterloo in 2021, focused on data protection in big data analysis.
Background
- Research interests: privacy (techniques, concerns, policies) in machine learning and data management, differential privacy, multi-party computation, and applied cryptography. Background: Worked on a variety of topics in security, privacy, and cryptography over the past decade in academia and industry.
Miscellany
- Welcomes any discussion and collaboration on the topic of impediments to the deployment of privacy-protecting techniques and ways to facilitate it for various industry sectors.
