Masoumeh Shafieinejad

Masoumeh Shafieinejad


Masoumeh’s research at the Vector Institute is on privacy techniques, concerns, and limitations in applied machine learning and data management. She is currently working on privacy preserving federated learning and synthetic data generation in collaboration with partners from finance and health sectors.

Throughout her career, Masoumeh has worked on a variety of topics in security, privacy, and cryptography in academia and industry, resulting in publications in both cryptography and data science venues. She received her PhD from the University of Waterloo on data protection in big data analysis. During the program, she conducted internships at National Research Council of Canada (NRC), and Microsoft Research (MSR). In her internships, she designed “a secure mechanism to perform join operation over encrypted data”, and “a privacy-preserving scheme for correlated data in social graphs” correspondingly.
Before joining the Vector Institute, Masoumeh was a senior cryptography consultant at Royal Bank of Canada, where she developed cryptographic architectural patterns & standards, and provided guidance for post-quantum cryptography migration.

Research Interests

  • Notions and Applications of Privacy
  • Privacy in Machine Learning
  • Applied Cryptography