- Fix syntax error in run.sh: remove extra quote and correct --log-path to --log-file
- Comment out HNSW algorithm implementation in enc.rs and algorithms.rs to simplify codebase
- Bump version to 0.3.1 in Cargo.toml
- Remove HNSW implementation guide and test files
- Add comprehensive project writeup documentation
- Replace FheUint12 with FheInt14 across all encryption operations
- Update scaling to use i16 instead of u32 for signed integer support
- Fix FheInt14 max value to 8191 (correct range limit)
- Optimize distance computation with parallel processing using rayon
- Simplify bitonic sort implementation by removing depth tracking
- Update plaintext version to match encrypted scaling behavior
- Add Default trait implementation for FheHnswGraph
- Remove generic type parameters from all structs and functions
- Unify to use only FheUint12 for all encryption operations
- Remove i32 encryption functions (encrypt_query_i32, encrypt_point_i32)
- Remove SupportedFheInt trait and implementations
- Remove data_bit_width command line parameter
- Simplify function signatures without complex trait bounds
- Clean up unused imports and variables
- Default algorithm changed to 'bitonic' for better performance
- Add rayon dependency for parallel processing
- Implement rayon::broadcast for proper TFHE server key distribution across threads
- Fix bitonic sort to handle non-power-of-2 input sizes by padding to nearest 2^k
- Add parallel execution for recursive calls and comparison operations
- Update API to accept encrypted max values for safe padding
- Add Send/Sync traits for EncryptedNeighbor to enable thread safety
- Modify perform_knn_selection to support bitonic sort requirements
- Bump version to 0.2.0
This resolves the bitonic sort correctness issue where non-power-of-2 array sizes
caused incorrect results. The algorithm now properly pads from 100 to 128 elements
and should produce deterministic, correct results.
- Create automated build script with version extraction and Aliyun registry push
- Add comprehensive HNSW implementation guide with step-by-step instructions
- Update Dockerfile to use musl target and enc binary for deployment
- Include performance optimization strategies and debugging tips
- Add HNSW search algorithm support with perform_hnsw_search function
- Implement optimal parameters (M=8, L=3, P=0.6) from systematic testing
- Replace broken search_layer with comprehensive TODO framework for manual implementation
- Add generic type support for both FheInt32 and FheUint12 operations
- Include debug mode, performance analysis, and implementation guide
- Restructure code into separate modules: data, algorithms, logging, cache
- Add efficient caching system for keys and encrypted distances
- Implement three sorting algorithms: selection, bitonic, heap
- Add comprehensive logging with timestamps and progress tracking
- Configure musl target for static compilation
- Support command-line algorithm selection and cache control
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit adds a complete FHE-based K-nearest neighbors implementation using TFHE:
Key Features:
- Encrypts training data and query vectors using FheInt32 and FheUint8
- Implements encrypted Euclidean distance calculation with 100x scaling for precision
- Uses bitonic sorting with encrypted conditional swaps for secure k-selection
- Includes comprehensive progress tracking and timing for long-running operations
- Memory optimizations: pre-allocated vectors and reused encrypted constants
Algorithm Implementation:
- Encrypted distance computation with homomorphic arithmetic operations
- Bitonic sort algorithm adapted for encrypted data structures
- Secure index tracking with encrypted FheUint8 values
- Select API usage for conditional swaps maintaining data privacy
Performance:
- Handles 100 training points with 10 dimensions in ~98 minutes on consumer hardware
- Includes detailed progress bars and time estimation
- Results validated against plain-text implementation (8/10 match rate)
Documentation:
- Comprehensive function documentation for all core algorithms
- Time complexity analysis and performance benchmarking notes
- Clear separation between client-side encryption/decryption and server-side computation
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add plain KNN implementation with JSONL data processing
- Create Docker deployment setup with python:3.13-slim base
- Add comprehensive OJ-style testing system with accuracy validation
- Update README with detailed scoring mechanism explanation
- Add run.sh script following competition manual requirements
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add manual.md documentation
- Include train.jsonl dataset for FHE-KNN training
- Update README.md with project details
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add Cargo.toml with TFHE, CSV, and Serde dependencies
- Add .gitignore for Rust target directory
- Include Iris dataset for machine learning experiments
- Add plain KNN implementation binary
- Update LICENSE to MIT and improve README
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
