aidotse/LeakPro
Feb 2025 – Apr 2025
3 Months active
Languages Used
Python
Technical Skills
Data SecurityMachine LearningPythonPython ProgrammingAdversarial AttacksCUDA
Daniel Nilsson
PROFILE
Daniel Nilsson
Daniel Nilsson developed and refined privacy and security features for the aidotse/LeakPro repository, focusing on membership inference attacks and GPU-accelerated adversarial example generation. He implemented the OSLO Membership Inference Attack using shadow models in Python, enabling privacy risk assessment and security auditing for machine learning deployments. Daniel enhanced the clarity and maintainability of the OSLO methodology through improved documentation and code structure, supporting reproducibility for security research. He also resolved a critical GPU execution issue by transferring labels to the CUDA device, leveraging CUDA and deep learning techniques to accelerate adversarial example generation and improve the reliability of GPU-based workflows.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
282
Activity Months3
Your Network
13 peopleSame Organization
@thep.lu.se1
Work History
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for aidotse/LeakPro focused on stabilizing and accelerating GPU-based adversarial example generation by transferring labels to the CUDA device. This work fixed the GPU execution path and unlocked CUDA-accelerated workflows, delivering measurable performance gains and stronger reliability.
1 Commits
Apr 1, 2025April 2025 monthly summary for aidotse/LeakPro focused on stabilizing and accelerating GPU-based adversarial example generation by transferring labels to the CUDA device. This work fixed the GPU execution path and unlocked CUDA-accelerated workflows, delivering measurable performance gains and stronger reliability.
April 2025
March 2025
1 Commits • 1 Features
Mar 1, 20252025-03 Monthly Summary for aidotse/LeakPro: The primary focus was refining the One-Shot Label-Only Membership Inference Attack (OSLO) description and its implementation in the LeakPro codebase to improve clarity, accuracy, and maintainability. This work enhances reproducibility and review readiness for security research and threat-model validation, enabling safer evaluation of the OSLO methodology while laying groundwork for future enhancements. No major bugs were fixed this month; activity centered on documentation and code clarity to support long-term business value.
March 2025
1 Commits • 1 Features
Mar 1, 20252025-03 Monthly Summary for aidotse/LeakPro: The primary focus was refining the One-Shot Label-Only Membership Inference Attack (OSLO) description and its implementation in the LeakPro codebase to improve clarity, accuracy, and maintainability. This work enhances reproducibility and review readiness for security research and threat-model validation, enabling safer evaluation of the OSLO methodology while laying groundwork for future enhancements. No major bugs were fixed this month; activity centered on documentation and code clarity to support long-term business value.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: OSLO Membership Inference Attack Implementation delivered for aidotse/LeakPro using shadow models to determine whether a data point was part of the training data of a target model. This feature enables privacy risk assessment, security auditing, and vulnerability benchmarking for ML deployments. The work was integrated into the LeakPro repo with the commit noted below and sets groundwork for additional privacy defenses and measurements.
1 Commits • 1 Features
Feb 1, 2025February 2025: OSLO Membership Inference Attack Implementation delivered for aidotse/LeakPro using shadow models to determine whether a data point was part of the training data of a target model. This feature enables privacy risk assessment, security auditing, and vulnerability benchmarking for ML deployments. The work was integrated into the LeakPro repo with the commit noted below and sets groundwork for additional privacy defenses and measurements.
February 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability93.4%
Architecture100.0%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Adversarial AttacksCUDAData SecurityDeep LearningMachine LearningPythonPython Programming
Repositories Contributed To
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