Papers · Private training · PriFT
PriFT
PriFT: Private Fine-Tuning using off-the-shelf MPC and HE libraries
Reported benchmarks
| mode | speedup vs fully private | accuracy retention |
|---|---|---|
| MPC semi-private | 3 | close to plaintext (no metric extracted) |
Notes
Uses a transformer as a frozen feature extractor, then trains a small NN on the privacy-protected features. Two modes: fully-private (encrypted end-to-end) and semi-private (labels decrypted during training for speed). Findings: MPC beats HE, especially semi-private; MPC semi-private is ~3x faster than fully-private; accuracy close to plaintext. Its value here is as a clean MPC-vs-HE comparison on one real task. PDF is behind Cloudflare -- reference list NOT extracted; do that before trusting the adjacent-literature list below.
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uses_zk_note: No proofs of any kind. PriFT provides PRIVACY (the data stays encrypted), not VERIFIABILITY (nobody proves the computation was correct). Orthogonal axis to this repo's other entries -- included to mark the boundary, not to compare. primitive: MPC (Crypten) and HE (TenSEAL), benchmarked head-to-head