Over nine months, Oren Shtman engineered core privacy analysis and attack workflows for the LeakPro repository, focusing on scalable membership inference attacks and robust reporting. He refactored configuration and execution logic to support multi-target model analysis, integrated advanced algorithms like BCJR MAP decoding in C with Python bindings, and expanded attack coverage with UMAP-based feature extraction. Leveraging Python, PyTorch, and C, Oren improved data handling, model training, and reporting pipelines, enabling reproducible privacy audits and efficient experimentation. His disciplined approach emphasized modular design, code quality, and maintainability, resulting in a flexible framework for evaluating machine learning model privacy and security.
October 2025: Focused on strengthening LeakPro's attack analysis framework and code quality. Key outcomes include: enhanced RaMIA attack with UMAP-based features and tuned parameters; MCA attack removal to reduce maintenance burden; audited/testing configurations for RMIA/BASE/RAMIA to support parameterized experimentation; added umap-learn dependency for dimensionality reduction; comprehensive code cleanup and refactor to improve maintainability and CI reliability.
October 2025: Focused on strengthening LeakPro's attack analysis framework and code quality. Key outcomes include: enhanced RaMIA attack with UMAP-based features and tuned parameters; MCA attack removal to reduce maintenance burden; audited/testing configurations for RMIA/BASE/RAMIA to support parameterized experimentation; added umap-learn dependency for dimensionality reduction; comprehensive code cleanup and refactor to improve maintainability and CI reliability.
September 2025 monthly summary for the LeakPro project focused on delivering core privacy analysis enhancements and foundational decoding capabilities, with an emphasis on business value and technical robustness.
September 2025 monthly summary for the LeakPro project focused on delivering core privacy analysis enhancements and foundational decoding capabilities, with an emphasis on business value and technical robustness.
Month: 2025-05 Key features delivered: - LeakPro: Multi-target model support for membership inference attacks. Refactored configuration and execution logic to handle multiple target models, including changes to data loading, model training, and attack execution. This enables running attacks against various target models simultaneously or sequentially, increasing the framework's utility for privacy analysis. Major bugs fixed: - No major bugs fixed this period. Overall impact and accomplishments: - Enables scalable privacy analysis by supporting multiple target models in a single workflow, increasing throughput and flexibility for evaluating model privacy. - Improves maintainability with modular configuration and execution paths, reducing future integration effort for additional target models. Technologies/skills demonstrated: - System refactoring for multi-model orchestration, configuration-driven workflows, and attack execution orchestration. - Data loading and model training adaptations to multi-target scenarios. - Python/ML tooling, software design for scalability and maintainability, and quality code practices.
Month: 2025-05 Key features delivered: - LeakPro: Multi-target model support for membership inference attacks. Refactored configuration and execution logic to handle multiple target models, including changes to data loading, model training, and attack execution. This enables running attacks against various target models simultaneously or sequentially, increasing the framework's utility for privacy analysis. Major bugs fixed: - No major bugs fixed this period. Overall impact and accomplishments: - Enables scalable privacy analysis by supporting multiple target models in a single workflow, increasing throughput and flexibility for evaluating model privacy. - Improves maintainability with modular configuration and execution paths, reducing future integration effort for additional target models. Technologies/skills demonstrated: - System refactoring for multi-model orchestration, configuration-driven workflows, and attack execution orchestration. - Data loading and model training adaptations to multi-target scenarios. - Python/ML tooling, software design for scalability and maintainability, and quality code practices.
April 2025 for aidotse/LeakPro focused on delivering end-to-end reporting capabilities, expanding analytics algorithms, and strengthening reliability. Key features delivered include report handler improvements with load integration, MIAResult constructor refactor, multi-attack loading/execution with RMIA workflow, Laplace and MCA algorithm additions, and factory/LSet enhancements. The month also emphasized code quality and testing through Ruff linting and expanded testing infrastructure, alongside targeted bug fixes affecting PDF saving/image rendering and test suites. Overall, these efforts improved business value by enabling faster, more reliable reporting, broader analytics capabilities, and increased developer velocity while reducing regression risk.
April 2025 for aidotse/LeakPro focused on delivering end-to-end reporting capabilities, expanding analytics algorithms, and strengthening reliability. Key features delivered include report handler improvements with load integration, MIAResult constructor refactor, multi-attack loading/execution with RMIA workflow, Laplace and MCA algorithm additions, and factory/LSet enhancements. The month also emphasized code quality and testing through Ruff linting and expanded testing infrastructure, alongside targeted bug fixes affecting PDF saving/image rendering and test suites. Overall, these efforts improved business value by enabling faster, more reliable reporting, broader analytics capabilities, and increased developer velocity while reducing regression risk.
March 2025 focused on end-to-end improvements in LeakPro, prioritizing CIFAR workflow enhancements, MIA evaluation reliability, testing robustness, and reporting consistency. Implemented a streamlined CIFAR handling path with data Transform support and introduced a ResNet18 CIFAR model; updated CIFAR-10 flow with auditing integration. Revamped MIA reporting with a unified result schema and improved ROC AUC handling, enabling consistent evaluation across LiRA, RMIA, QMIA, YOQO, HSJ, and related attacks. Strengthened testing infrastructure, added notebook execution tweaks, new dataset classes, and more robust model utilities. Reorganized reporting modules and result classes to improve PDF/report generation and configuration handling. These changes improve training efficiency, evaluation reliability, and reporting scalability for stakeholders.
March 2025 focused on end-to-end improvements in LeakPro, prioritizing CIFAR workflow enhancements, MIA evaluation reliability, testing robustness, and reporting consistency. Implemented a streamlined CIFAR handling path with data Transform support and introduced a ResNet18 CIFAR model; updated CIFAR-10 flow with auditing integration. Revamped MIA reporting with a unified result schema and improved ROC AUC handling, enabling consistent evaluation across LiRA, RMIA, QMIA, YOQO, HSJ, and related attacks. Strengthened testing infrastructure, added notebook execution tweaks, new dataset classes, and more robust model utilities. Reorganized reporting modules and result classes to improve PDF/report generation and configuration handling. These changes improve training efficiency, evaluation reliability, and reporting scalability for stakeholders.
February 2025 highlights for aidotse/LeakPro: key branding updates, schema-driven RMIA improvements, new data supports for funding and research, strengthened code quality and tests, and an Optuna-based offline optimization workflow, plus updated documentation. These changes deliver clearer branding, improved data validation and interoperability, safer deployments, faster experimentation cycles, and easier customer onboarding.
February 2025 highlights for aidotse/LeakPro: key branding updates, schema-driven RMIA improvements, new data supports for funding and research, strengthened code quality and tests, and an Optuna-based offline optimization workflow, plus updated documentation. These changes deliver clearer branding, improved data validation and interoperability, safer deployments, faster experimentation cycles, and easier customer onboarding.
January 2025 monthly summary for aidotse/LeakPro: Delivered a stabilized CI workflow and robust test infrastructure, enabling faster feedback and higher reliability. The team also reduced noise by removing MIA-specific tests to restore a focused baseline. Key outcomes include matrix-based test execution, consolidated reporting, and standardized development dependencies, driving more predictable PR cycles and release readiness. Technologies demonstrated include CI/CD optimization, test automation design, environment management, and disciplined codebase cleanup with clear rollback practices.
January 2025 monthly summary for aidotse/LeakPro: Delivered a stabilized CI workflow and robust test infrastructure, enabling faster feedback and higher reliability. The team also reduced noise by removing MIA-specific tests to restore a focused baseline. Key outcomes include matrix-based test execution, consolidated reporting, and standardized development dependencies, driving more predictable PR cycles and release readiness. Technologies demonstrated include CI/CD optimization, test automation design, environment management, and disciplined codebase cleanup with clear rollback practices.
December 2024 monthly summary for aidotse/LeakPro focused on modernizing CI/CD and configuration management, expanding test quality practices, and integrating metrics for improved model evaluation. Implemented TOML-based CI/CD installation and migration, enabling consistent, faster deployments and easier dependency management. Established code coverage infrastructure with a dedicated folder and badges, improving visibility into test coverage. Integrated TorchMetrics for richer, more actionable metrics. Updated the factory module to TOML configuration for unified behavior across components. Removed Python version pin to reduce environment constraints and stabilize the dev workflow, complemented by test stabilization and dev-dependency fixes. Cleaned up CI/CD configuration and updated documentation for better maintainability.
December 2024 monthly summary for aidotse/LeakPro focused on modernizing CI/CD and configuration management, expanding test quality practices, and integrating metrics for improved model evaluation. Implemented TOML-based CI/CD installation and migration, enabling consistent, faster deployments and easier dependency management. Established code coverage infrastructure with a dedicated folder and badges, improving visibility into test coverage. Integrated TorchMetrics for richer, more actionable metrics. Updated the factory module to TOML configuration for unified behavior across components. Removed Python version pin to reduce environment constraints and stabilize the dev workflow, complemented by test stabilization and dev-dependency fixes. Cleaned up CI/CD configuration and updated documentation for better maintainability.
November 2024 monthly summary for aidotse/LeakPro: Consolidated dependency management and environment configuration for subpackages, aligned core dependencies via pyproject.toml, and streamlined project setup by removing obsolete configurations. Established environment config templates for subpackages (env_mia.yml, env_synthetic.yml, env_federated.yml) enabling reproducible development environments. Initiated subpackage scaffolding for mia, synthetic, and federated to support modular development and faster onboarding.
November 2024 monthly summary for aidotse/LeakPro: Consolidated dependency management and environment configuration for subpackages, aligned core dependencies via pyproject.toml, and streamlined project setup by removing obsolete configurations. Established environment config templates for subpackages (env_mia.yml, env_synthetic.yml, env_federated.yml) enabling reproducible development environments. Initiated subpackage scaffolding for mia, synthetic, and federated to support modular development and faster onboarding.
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