aidotse/LeakPro
Oct 2024 – Jul 2025
5 Months active
Languages Used
TOMLPythonGit ConfigurationShell
Technical Skills
Dependency ManagementBackend DevelopmentBug FixingData PrivacyData ProcessingDeep Learning
mattiasakesson
PROFILE
Mattiasakesson
Over five months, Ma10s developed and enhanced the LeakPro repository, focusing on backend systems for privacy-focused machine learning research. They engineered a unified Gradient Inversion Attack framework with cross-modal support, refactored attack logic for stability, and introduced robust data preprocessing modules for text sequence labeling. Using Python and PyTorch, Ma10s implemented standardized model evaluation with torchmetrics, improved reporting accuracy, and ensured data integrity across multi-batch datasets. Their work included dependency management, code refactoring, and advanced data handling, resulting in a maintainable, modular codebase that accelerates research iteration and supports scalable analytics for security and privacy attack outcomes.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
21Total
Bugs
2
Commits
21
Features
8
Lines of code
2,388
Activity Months5
Your Network
11 people
Work History
July 2025
3 Commits • 2 Features
Jul 1, 2025July 2025 monthly highlights for aidotse/LeakPro: Implemented robust text saving, reporting enhancements for attack results, and strengthened data integrity across multi-batch datasets. The changes improve reporting accuracy, reduce manual post-processing, and provide scalable foundations for analytics on attack outcomes.
3 Commits • 2 Features
Jul 1, 2025July 2025 monthly highlights for aidotse/LeakPro: Implemented robust text saving, reporting enhancements for attack results, and strengthened data integrity across multi-batch datasets. The changes improve reporting accuracy, reduce manual post-processing, and provide scalable foundations for analytics on attack outcomes.
July 2025
June 2025
8 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for aidotse/LeakPro. Key features and bugs delivered focused on enhancing the Gradient Inversion Attack (GIA) research framework and strengthening the data pipeline for text sequence labeling. The work emphasizes cross-modal capabilities, maintainability, and readiness for PR review, delivering tangible business value for security research and tooling stability. Key features delivered: - Unified GIA attack framework enhancements with a centralized generic attack loop, improved loss calculation, logging, gradient handling, and support for text data via GiaNER extension. Refactors include integration with run_gia_attack, class refinements for InvertingGradients, and overall code quality improvements. - Data preprocessing modules and dataset integration for sequence labeling, introducing a data_ module with tokenization, label alignment utilities, and training example creation. Removal of obsolete get_at_text functionality and updates to repo ignore rules for datasets. Major bugs fixed: - Ruff linting fixes across GIA components, addressing CI issues and improving code quality. - Cleanup of the data pipeline by removing deprecated get_at_text usage and tightening dataset preprocessing to prevent misalignment issues. Overall impact and accomplishments: - Enabled cross-modal GIA experimentation and faster iteration by delivering a cohesive framework and robust data preparation utilities, increasing research velocity while reducing maintenance overhead. - Established PR readiness and better collaboration readiness through improved modularization and repository hygiene. Technologies/skills demonstrated: - Python, modular architecture, and refactoring (Unified GIA loop, InvertingGradients) ; data engineering (text sequence labeling preprocessing) ; testing/QA hygiene (ruff lint fixes, CI alignment).
June 2025
8 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for aidotse/LeakPro. Key features and bugs delivered focused on enhancing the Gradient Inversion Attack (GIA) research framework and strengthening the data pipeline for text sequence labeling. The work emphasizes cross-modal capabilities, maintainability, and readiness for PR review, delivering tangible business value for security research and tooling stability. Key features delivered: - Unified GIA attack framework enhancements with a centralized generic attack loop, improved loss calculation, logging, gradient handling, and support for text data via GiaNER extension. Refactors include integration with run_gia_attack, class refinements for InvertingGradients, and overall code quality improvements. - Data preprocessing modules and dataset integration for sequence labeling, introducing a data_ module with tokenization, label alignment utilities, and training example creation. Removal of obsolete get_at_text functionality and updates to repo ignore rules for datasets. Major bugs fixed: - Ruff linting fixes across GIA components, addressing CI issues and improving code quality. - Cleanup of the data pipeline by removing deprecated get_at_text usage and tightening dataset preprocessing to prevent misalignment issues. Overall impact and accomplishments: - Enabled cross-modal GIA experimentation and faster iteration by delivering a cohesive framework and robust data preparation utilities, increasing research velocity while reducing maintenance overhead. - Established PR readiness and better collaboration readiness through improved modularization and repository hygiene. Technologies/skills demonstrated: - Python, modular architecture, and refactoring (Unified GIA loop, InvertingGradients) ; data engineering (text sequence labeling preprocessing) ; testing/QA hygiene (ruff lint fixes, CI alignment).
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 — Key enhancements to Gradient Inversion Attack tooling in LeakPro: refactored the attack logic with a new inference closure and adjusted the learning rate scheduler to boost reconstruction accuracy and stability. Added get_at_text to prepare a reconstruction dataset with random noise images, applied label-based embedding adjustments, and registered a gradient masking hook to ensure only selected embedding components are trainable, returning embeddings and a reconstruction DataLoader. Commits include 7881c2f57b3966dfe6a3836b8bd7ecf3fb49a709 (store) and 36bc7a48c01da837cc9fdc782194684f3dc6e59b (add get_at_text).
2 Commits • 1 Features
May 1, 2025May 2025 — Key enhancements to Gradient Inversion Attack tooling in LeakPro: refactored the attack logic with a new inference closure and adjusted the learning rate scheduler to boost reconstruction accuracy and stability. Added get_at_text to prepare a reconstruction dataset with random noise images, applied label-based embedding adjustments, and registered a gradient masking hook to ensure only selected embedding components are trainable, returning embeddings and a reconstruction DataLoader. Commits include 7881c2f57b3966dfe6a3836b8bd7ecf3fb49a709 (store) and 36bc7a48c01da837cc9fdc782194684f3dc6e59b (add get_at_text).
May 2025
March 2025
7 Commits • 2 Features
Mar 1, 2025March 2025: Delivered key GIA enhancements in aidotse/LeakPro to extend attack capabilities into text modalities and improve robustness. Added OneHotBERT-based text processing with NER/PII support and a practical example script for text embedding attacks. Strengthened GIA core with a smaller learning rate and enhanced closure/class support to improve attack stability. Fixed robustness issues when image data is unavailable and corrected a post-optimization reconstruction data bug to ensure best results are correctly tracked. These efforts increased research reproducibility, expanded applicability to privacy-focused scenarios, and reduced runtime errors in attack loops.
March 2025
7 Commits • 2 Features
Mar 1, 2025March 2025: Delivered key GIA enhancements in aidotse/LeakPro to extend attack capabilities into text modalities and improve robustness. Added OneHotBERT-based text processing with NER/PII support and a practical example script for text embedding attacks. Strengthened GIA core with a smaller learning rate and enhanced closure/class support to improve attack stability. Fixed robustness issues when image data is unavailable and corrected a post-optimization reconstruction data bug to ensure best results are correctly tracked. These efforts increased research reproducibility, expanded applicability to privacy-focused scenarios, and reduced runtime errors in attack loops.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 focused on enabling standardized model evaluation and performance reporting for LeakPro by integrating the torchmetrics library into dependencies, laying the groundwork for downstream dashboards and reports. This foundational work improves model monitoring, accelerates data-driven decision-making, and aligns ML evaluation with industry best practices across the product.
1 Commits • 1 Features
Oct 1, 2024October 2024 focused on enabling standardized model evaluation and performance reporting for LeakPro by integrating the torchmetrics library into dependencies, laying the groundwork for downstream dashboards and reports. This foundational work improves model monitoring, accelerates data-driven decision-making, and aligns ML evaluation with industry best practices across the product.
October 2024
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Quality Metrics
Correctness82.8%
Maintainability82.8%
Architecture78.2%
Performance65.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
Git ConfigurationPythonShellTOML
Technical Skills
Backend DevelopmentBug FixingCode RefactoringCode RemovalData AugmentationData HandlingData LoadingData PreprocessingData PrivacyData ProcessingData SerializationData ValidationDebuggingDeep LearningDependency Management
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