
Developed privacy-preserving graph and manifold learning modules for the secretflow/spu repository, focusing on secure analytics for enterprise environments. The work introduced implementations of Dijkstra’s algorithm, Isomap, and Spectral Embedding, as well as a privacy-preserving variant of Floyd-Warshall, all designed to operate within secure multi-party computation frameworks. Leveraging Python and Bazel, the developer ensured that each module aligned with strict privacy requirements while maintaining performance goals. Comprehensive end-to-end tests and emulations were added to validate both privacy guarantees and correctness, resulting in robust, production-ready features that enable secure dimensionality reduction and graph analytics in privacy-sensitive applications.
April 2025 — Delivered privacy-preserving graph and manifold learning capabilities in secretflow/spu, with new modules for Dijkstra, Isomap, and Spectral Embedding, alongside a privacy-preserving Floyd-Warshall variant. Implemented end-to-end tests and emulations to verify privacy guarantees. Focused on enabling secure graph analytics with enterprise-grade privacy.
April 2025 — Delivered privacy-preserving graph and manifold learning capabilities in secretflow/spu, with new modules for Dijkstra, Isomap, and Spectral Embedding, alongside a privacy-preserving Floyd-Warshall variant. Implemented end-to-end tests and emulations to verify privacy guarantees. Focused on enabling secure graph analytics with enterprise-grade privacy.

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