arXiv AI
BR-MoE: Budgeted Semantic Release for Privacy-Preserving Traffic Analytics
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Sep 1
Defending Wearable VLMs Against Private Attribute Inference
arXiv:2608.28691v1 Announce Type: cross Abstract: Wearable VLM pipelines promise continuous multimodal assistance from egocentric visual capture: a user asks a task-driven question about the surround...
arXiv Machine Learning
Sep 25
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning
Calpric is a system that combines automatic text selection, segmentation, active learning, and crowdsourced annotation to create a large, balanced training set for privacy policy classification. By simplifying the labeling task, it enables untrained crowd workers to match the performance of trained annotators and reduces inter‑annotator disagreement, cutting labeling costs. The approach yields a dataset of 16,000 policy text segments across nine data categories and produces models that deliver accurate, fine‑grained labels at a cost of roughly $0.92–$1.71 per segment.
arXiv Machine Learning
Sep 22
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning
arXiv:2401.08038v2 Announce Type: replace-cross Abstract: A significant challenge to training accurate deep learning models on privacy policies is the cost and difficulty of obtaining a large and com...
arXiv Machine Learning
Sep 16
Differentially Private Semantic Plans for Aggregate Insight Generation
The paper introduces ‘DP-SPIN’, a trusted‑curator framework that generates differentially private semantic plans for aggregate insight generation. ‘DP-SPIN’ maps each record to a bounded sparse nonnegative vector over pre‑defined semantic concepts, sums these vectors into a semantic sketch, and releases a noisy plan containing admitted concepts and their masses. The framework provides user‑level privacy by clipping each user’s contribution and ensures that the final summary is differentially private through post‑processing, with guarantees established under both add/drop and replacement adjacency.
arXiv Computation and Language
Oct 16
QuasiLeak-Bench: Evaluating LLM-Based Privacy Detectors on Policy-Conditioned Quasi-Evidence Group Recovery
QuasiLeak-Bench is a new benchmark that evaluates large language model (LLM) based privacy detectors on their ability to recover quasi‑evidence groups—sets of weak quasi‑identifiers that jointly reveal private attributes—while respecting sender‑specified disclosure policies. The benchmark uses a reverse‑construction pipeline to generate documents containing verified quasi‑evidence groups and applies automated quality control to produce reliable ground truth at scale. Experiments show that current privacy detectors perform poorly on this task, but a fine‑tuned 4B LLM can outperform the best prompted models, indicating the task is learnable.
arXiv Machine Learning
Aug 5
DP-MemView: A Memory Interface for Attribute-Level Transcript Privacy in Long-Term LLM Agents
arXiv:2608. 03130v1 Announce Type: cross Abstract: Long-term memory enables persistent personalization in LLM agents, but repeated memory-conditioned responses can cumulatively reveal protected attributes even when they are never stated explicitly.