pllmR20: Oblivious Ciphertext Compression via Linear Codes
This work develops oblivious ciphertext compression using linear codes and syndrome decoding. PLLM status: tracked.
Status: tracked
Year: 2026
Authors: Pascal Giorgi, Bruno Grenet, Mark Simkin
External source: Oblivious Ciphertext Compression via Linear Codes
Source contribution
This work develops oblivious ciphertext compression using linear codes and syndrome decoding.
PLLM perspective
Reproduce the exact source construction and its decoding-side-information assumptions. Bind codecs to representations and valid domains; compare the PLLM syndrome design as an adaptation.
Network archives contain a custom bounded-overflow codec, not this paper's implementation.
This paper remains tracked. Named modules below are implementation targets, not finished PLLM components; no reproduction, integration, assurance, or matched benchmark is claimed.
The source's boundary remains binding: Lossless compression is conditional on its domain and side information. Successful decode is not proof of correct inference, and adaptive retries can leak.
PLLM modules and documentation
Target module identifiers: codec.oblivious_linear_code, codec.domain_certificate. These identifiers describe scoped work unless this page explicitly links an implementation source. Dependencies: None.
Backlog
This source is assigned to reimplementation priority 7, Preparation, compression, and acceleration. Its source-registry order is 20. Follow current gates and unfinished work in the research backlog.
R19: Private and Verifiable Outsourcing of Open-Weight LLM Inference
The abstract describes private and verifiable open-weight inference with two malicious, non-colluding servers and model-owner-published information. PLLM status: tracked.
R21: NEXUS: Secure Transformer Inference Made Non-interactive
NEXUS combines approximate HE with lightweight garbled circuits for non-interactive two-party transformer forward inference. PLLM status: tracked.