Oblivious

Engineer

• Privacy Accounting & Mechanisms: Implement and analyse privacy loss accountants (RDP, zCDP) and their conversions to (ϵ, δ)-DP. Calibrate and apply noise mechanisms (Gaussian, Laplace) based on rigorous sensitivity analysis.
• Differentially Private SQL Engine: Develop algorithms for static and dynamic sensitivity analysis of relational operators. Build query rewriting logic to inject calibrated noise and manage a per-user privacy budget ledger.
• Compiler & Static Analysis: Use Python AST manipulation and static analysis to enforce a DP-safe execution environment, ensuring user-submitted code cannot leak private information.
• DP Synthetic Data: Implement and benchmark state-of-the-art algorithms (e.g., MWEM, PGM, PrivBayes variants) for high-dimensional synthetic data generation, analysing their privacy-utility trade-offs.

Algorithms Engineer, Differential Privacy

• Design, implement and evaluate privacy-preserving algorithms.
• Implement and integrate various differential privacy mechanisms.
• Calibrate and apply noise mechanisms (Gaussian, Laplace) based on rigorous sensitivity analysis.
• Use Python AST manipulation and static analysis to enforce a DP-safe execution environment, ensuring user-submitted code cannot leak private information.
• Privacy Accounting & Mechanisms: Analyse and implement privacy accounting and mechanisms.
• DP Synthetic Data: Implement and benchmark state-of-the-art algorithms for high-dimensional synthetic data generation.
• Collaborate with cross-functional teams to design, develop, and deploy privacy-preserving systems.