aidotse/LeakPro
Nov 2024 – Nov 2024
1 Month active
Languages Used
JinjaPyTorchPythonYAML
Technical Skills
Data AnalysisData HandlingData PrivacyDeep LearningMachine LearningMembership Inference Attacks
Daniel Nilsson
PROFILE
Daniel Nilsson
Daniel Nilsson developed an end-to-end YOQO Membership Inference Attack feature for the aidotse/LeakPro repository, focusing on enhancing security assessment capabilities. He integrated configurable audit YAML options, allowing for reproducible and controlled attack evaluations. Using PyTorch and Python, Daniel optimized the attack logic to support both online and offline scenarios, addressing data preparation, objective calculation, and target-output handling. He improved code maintainability by resolving lint issues and incorporating reviewer feedback, ensuring production readiness. This work deepened the project’s support for data privacy and model auditing, providing a robust foundation for measuring leakage risks in machine learning models.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
547
Activity Months1
Your Network
13 people
Work History
November 2024
4 Commits • 1 Features
Nov 1, 20242024-11 Summary for aidotse/LeakPro: Delivered an end-to-end YOQO Membership Inference Attack feature with configurable audit YAML integration, enabling controlled, reproducible security assessments. Optimized attack logic for online and offline evaluation, including data preparation, objective calculation, and target-output handling in PyTorch-based evaluation. Enhanced code quality and collaboration by addressing reviewer comments and fixing lint issues to improve maintainability and readiness for production use. This work strengthens leakage risk measurement capabilities and accelerates security validation cycles for downstream products.
4 Commits • 1 Features
Nov 1, 20242024-11 Summary for aidotse/LeakPro: Delivered an end-to-end YOQO Membership Inference Attack feature with configurable audit YAML integration, enabling controlled, reproducible security assessments. Optimized attack logic for online and offline evaluation, including data preparation, objective calculation, and target-output handling in PyTorch-based evaluation. Enhanced code quality and collaboration by addressing reviewer comments and fixing lint issues to improve maintainability and readiness for production use. This work strengthens leakage risk measurement capabilities and accelerates security validation cycles for downstream products.
November 2024
Activity
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Quality Metrics
Correctness82.6%
Maintainability82.6%
Architecture82.6%
Performance72.6%
AI Usage25.0%
Skills & Technologies
Programming Languages
JinjaPyTorchPythonYAML
Technical Skills
Data AnalysisData HandlingData PrivacyDeep LearningMachine LearningMembership Inference AttacksModel AuditingModel OptimizationPyTorchPython
Repositories Contributed To
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