secretflow/spu
Apr 2025 – Oct 2025
6 Months active
Languages Used
PythonC++JAXMLIRBazel
Technical Skills
Bug FixingPython DevelopmentBuild Systems (Bazel)C++Data PrivacyJAX
周金金
PROFILE
周金金
Jinjin Zhou contributed to the secretflow/spu repository by developing privacy-preserving data aggregation features and enhancing machine learning workflows. Over six months, Zhou delivered the private groupby sum functionality in C++ for the SPU kernel, enabling secure aggregation with integrated tests and build system updates. Zhou also improved reproducibility in SML K-Means clustering by aligning outputs with scikit-learn and ensuring deterministic experiments using Python and JAX. Additional work included modularizing the SML library, upgrading JAX/Flax dependencies for stability, and implementing plaintext reveal features for early-stopping in logistic regression. The work demonstrated depth in kernel development, modularization, and secure computation.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
6Total
Bugs
2
Commits
6
Features
4
Lines of code
34,742
Activity Months6
Your Network
134 people
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for the SPU project focused on delivering a privacy-preserving data aggregation capability. Delivered the private groupby sum functionality for the SPU kernel by introducing new C++ sources and headers for group-by aggregation, implementing the private groupby sum logic, and adding tests. Updated build configuration and prot_wrapper to integrate the new functionality, enabling seamless usage in downstream workflows. Commit reference: 6c0b158173c0c5861356d4682d1844336e56f5cd.
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for the SPU project focused on delivering a privacy-preserving data aggregation capability. Delivered the private groupby sum functionality for the SPU kernel by introducing new C++ sources and headers for group-by aggregation, implementing the private groupby sum logic, and adding tests. Updated build configuration and prot_wrapper to integrate the new functionality, enabling seamless usage in downstream workflows. Commit reference: 6c0b158173c0c5861356d4682d1844336e56f5cd.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for secretflow/spu focusing on key accomplishments, stability, and business value.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for secretflow/spu focusing on key accomplishments, stability, and business value.
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for secretflow/spu focusing on modularity and maintainability improvements through SML library decoupling and standalone migration. No major bugs fixed this month. The work reduces SPU code coupling, simplifies tests, and paves the way for independent SML development.
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for secretflow/spu focusing on modularity and maintainability improvements through SML library decoupling and standalone migration. No major bugs fixed this month. The work reduces SPU code coupling, simplifies tests, and paves the way for independent SML development.
August 2025
June 2025
1 Commits
Jun 1, 2025June 2025 monthly summary focusing on the SPU repo: secretflow/spu. This period centered on improving the reliability and reproducibility of the SML K-Means algorithm, with targeted fixes that align SML results with scikit-learn benchmarks and ensure deterministic experiment setups for easier validation and auditing.
June 2025
1 Commits
Jun 1, 2025June 2025 monthly summary focusing on the SPU repo: secretflow/spu. This period centered on improving the reliability and reproducibility of the SML K-Means algorithm, with targeted fixes that align SML results with scikit-learn benchmarks and ensure deterministic experiment setups for easier validation and auditing.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for secretflow/spu focusing on key accomplishments and business impact. Delivered the SML reveal feature that enables plaintext access to SPU computations with early-stopping integration, including usage examples for conditional logic and loops, and integrated this capability into the logistic regression workflow to support early stopping. Concurrently refactored build dependencies and dataset loading utilities to improve build reliability and developer experience.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for secretflow/spu focusing on key accomplishments and business impact. Delivered the SML reveal feature that enables plaintext access to SPU computations with early-stopping integration, including usage examples for conditional logic and loops, and integrated this capability into the logistic regression workflow to support early stopping. Concurrently refactored build dependencies and dataset loading utilities to improve build reliability and developer experience.
May 2025
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for secretflow/spu: Delivered a critical bug fix for the Stax NN example by correcting the import path for the models module, eliminating ModuleNotFoundError and enabling the stax_nn script to locate and load model definitions and run as intended.
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for secretflow/spu: Delivered a critical bug fix for the Stax NN example by correcting the import path for the models module, eliminating ModuleNotFoundError and enabling the stax_nn script to locate and load model definitions and run as intended.
Activity
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Quality Metrics
Correctness91.6%
Maintainability88.4%
Architecture90.0%
Performance81.6%
AI Usage30.0%
Skills & Technologies
Programming Languages
BazelC++JAXMLIRPython
Technical Skills
Bug FixingBuild Systems (Bazel)C++ClusteringCode RefactoringData PrivacyData ScienceDependency ManagementJAXKernel DevelopmentMLIRMachine LearningModularizationPerformance OptimizationPrivate Aggregation
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