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
Developed an end-to-end YOQO Membership Inference Attack feature for the aidotse/LeakPro repository, enabling controlled and reproducible security assessments through configurable audit YAML integration. Leveraged Python and PyTorch to optimize the attack logic for both online and offline evaluation scenarios, focusing on efficient data preparation, objective calculation, and target-output handling. Addressed code quality by resolving lint issues and incorporating reviewer feedback to enhance maintainability and production readiness. This work strengthened the project’s capabilities in measuring model leakage risk and accelerated security validation cycles, drawing on skills in data privacy, machine learning, and model auditing to deliver a robust, configurable solution.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
547
Activity Months1
Your Network
16 peopleSame Organization
@ai.se2
Shared Repositories
14
Daniel NilssonMember
TheColdIceMember
fazelehhMember
Guillermo PadresMember
henrikfoMember
HevarooMember
Lucas BeerensMember
mattiasakessonMember
MuhaddisabaratMember
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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