google/heir
Mar 2025 – Mar 2025
1 Month active
Languages Used
C++GoMLIR
Technical Skills
C++ DevelopmentHomomorphic EncryptionMLIR DevelopmentSoftware ArchitectureTesting
Mark Gall
PROFILE
Mark Gall
Mark Gall developed a noise analysis model for BGV canonical embedding in the google/heir repository, focusing on lattice-based homomorphic encryption. He designed and implemented new C++ sources and headers, integrated the feature with the build system, and created parameter generation and validation transforms. His work included test examples to ensure correctness and reliability. By enhancing the benchmarking and analysis capabilities for the BGV scheme, Mark enabled deeper noise analysis and more effective parameter tuning. Leveraging skills in C++ development, MLIR, and software architecture, he delivered a robust feature that supports more reliable deployment decisions for homomorphic encryption applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
526
Activity Months1
Your Network
64 peopleSame Organization
@aisec.fraunhofer.de6
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025: Implemented Noise Analysis Model for BGV Canonical Embedding in google/heir, enabling deeper noise analysis, benchmarking, and enhanced tooling for parameter tuning in lattice-based HE. Includes new C++ sources/headers, build system integration, parameter generation/validation transforms, and test examples. This work enhances analysis accuracy and user-facing tooling, supporting more reliable deployment decisions.
1 Commits • 1 Features
Mar 1, 2025March 2025: Implemented Noise Analysis Model for BGV Canonical Embedding in google/heir, enabling deeper noise analysis, benchmarking, and enhanced tooling for parameter tuning in lattice-based HE. Includes new C++ sources/headers, build system integration, parameter generation/validation transforms, and test examples. This work enhances analysis accuracy and user-facing tooling, supporting more reliable deployment decisions.
March 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++GoMLIR
Technical Skills
C++ DevelopmentHomomorphic EncryptionMLIR DevelopmentSoftware ArchitectureTesting
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