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
Developed a noise analysis model for BGV canonical embedding in the google/heir repository, focusing on enhancing analysis accuracy and user-facing tooling for lattice-based homomorphic encryption. The work involved creating new C++ source and header files, integrating the feature with the build system, and implementing parameter generation and validation transforms. Leveraging expertise in C++ development, MLIR, and software architecture, the developer enabled deeper noise analysis and benchmarking capabilities for the BGV scheme. Comprehensive test examples were added to streamline validation, supporting more reliable parameter tuning and deployment decisions. This contribution improved the robustness and usability of BGV noise analysis workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
526
Activity Months1
Your Network
73 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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