Model Detail
privacy-filter
—privacy-filter is a large language model with 700M parameters released by Open-OSS. The model is registered under the token-classification pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
privacy-filter ships with 700M parameters. Total weight footprint is approximately 1.4 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
privacy-filter is best fit for general-purpose chat and instruction-following workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Privacy-Preserving Personalized Federated Learning (PPP-FL) for Medical Imaging
arXiv:2610.27470v1 Announce Type: cross Abstract: Federated learning (FL) is a distributed machine learning paradigm that reduces transmission costs, mitigates cross-border data sharing restrictions, and enhances privacy by keeping raw data on local devices. Clients in FL systems train local models
LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections (Full Version)
arXiv:2605.13265v2 Announce Type: replace Abstract: Split learning (SL) enables collaborative training by partitioning a neural network across clients and a central server. However, the cut-layer interface introduces a key challenge: high-dimensional activations incur substantial communication overh
BR-MoE: Budgeted Semantic Release for Privacy-Preserving Traffic Analytics
arXiv:2610.27026v1 Announce Type: cross Abstract: Traffic cameras support useful analysis, but their outputs can reveal private information. Replacing images with text reduces direct visual exposure. It does not place a formal bound on disclosure. The published reinforcement learning and mixture-of-
Reliable Estimation of Differential Privacy under Selection Bias
arXiv:2610.27021v1 Announce Type: cross Abstract: Statistical privacy audits are essential for verifying the theoretical privacy guarantees of differentially private (DP) mechanisms. Auditors typically use Membership Inference Attacks (MIAs) to estimate a lower bound on the true privacy leakage of D
LLM-Attack Suite: A Unified Red-Team Framework for Privacy Evaluation of Split and Distributed LLM Systems
arXiv:2610.22722v2 Announce Type: replace-cross Abstract: Split language models keep input text on a client but send intermediate representations to a server. Attacks can reconstruct text from these messages. Comparing privacy evaluations is difficult when attackers see different messages, know diff
Profile-DP: Differential Privacy for User-Controlled Profile Release in Personalized LLM Inference
arXiv:2610.27213v1 Announce Type: cross Abstract: Personalized large language models (LLMs) increasingly rely on profile-conditioned inference, pairing public requests with rich, user-specific facts. This paradigm exposes the user profile itself, together with the user's explicit sharing preferences
