R23: MPCache: MPC-Friendly KV Cache Eviction for Efficient Private Large Language Model Inference
MPCache evaluates MPC-friendly static and dynamic KV-cache eviction in a three-party SecretFlow/SPU setting; its artifact focuses state selection and operator costs. PLLM status: PLLM reimplementation.
Status: PLLM reimplementation
Year: 2025
Authors: Wenxuan Zeng, Ye Dong, Jinjin Zhou, Junming Ma, Jin Tan, Runsheng Wang, Meng Li
External source: MPCache: MPC-Friendly KV Cache Eviction for Efficient Private Large Language Model Inference
Source contribution
MPCache evaluates MPC-friendly static and dynamic KV-cache eviction in a three-party SecretFlow/SPU setting; its artifact focuses state selection and operator costs.
PLLM perspective
Reproduce cached generation and use its explicit KV state as a comparison oracle for PLLM decoder state without inferring complete protected boundary coverage.
No MPCache reproduction has been executed. Current PLLM work implements a deterministic structural DecoderPlan adaptation for dense Qwen2/Qwen3 fixed-shape caches; protected execution, fidelity, assurance, and matched benchmarks remain unchecked.
PLLM implements only a structural DecoderPlan adaptation. It does not reproduce MPCache's three-party protected runtime. Fidelity, protected execution, assurance, and matched benchmark status remain unchecked.
The source's boundary remains binding: Three online semi-honest parties and incomplete protected-boundary evidence are incompatible with the one-evaluator complete-model target.
PLLM modules and documentation
Target module identifiers: protocol.mpcache, state.secret_shared_kv, gate.mpcache_attention. These identifiers describe scoped work unless this page explicitly links an implementation source. Dependencies: None.
- Research backlog
- Reimplementation backlog
- Clean-room workflow
- PLLM method status
- Decoder plan usage
- MPCache structural adaptation source
Backlog
This source is assigned to reimplementation priority 5, Protected KV-cache policy. Its source-registry order is 23. Follow current gates and unfinished work in the research backlog.
R22: BumbleBee: Secure Two-party Inference Framework for Large Transformers
BumbleBee implements secure two-party transformer inference with specialized nonlinear protocols and an Apache-2.0 artifact. PLLM status: tracked.
R24: Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation
Maverick delegates matrix-vector multiplication with transparent preprocessing, information-theoretic batch verification, and LPN-based pseudorandom input masking while adding virtually no server-side verification overhead. PLLM status: planned.