The paper presents a privacy‑preserving text inference framework using CKKS homomorphic encryption for health‑misinformation classification and semantic retrieval. It combines BioBERT, MPNet, and TF‑IDF features for classification, achieving an accuracy of 0.971 and macro‑F1 of 0.950, while a late‑fusion similarity model reaches a Pearson correlation of 0.8691. Optimized ciphertext retrieval reduces latency from 222.028 s to 5.168 s and communication overhead from 6.11 GB to 2.13 MB over 7,128 items.
arXiv:2609.36376v1 Announce Type: cross Abstract: Dense retrieval, the key component of Retrieval Augmented Generation (RAG), retrieves the most relevant documents by comparing dense vector represent...
The paper introduces SHAQ, a defense called Shadow Query Generation that protects document embeddings in vector databases from embedding inversion attacks. SHAQ replaces direct embeddings with diverse shadow queries generated by a language model, thereby decomposing document semantics and decoupling stored embeddings from the original text. Experiments on various IR datasets show that SHAQ significantly lowers recovery rates, defends more tokens than baseline methods, and even improves retrieval utility.
With the widespread applications of large language models (LLMs), privacy-preserving inference has become increasingly essential for sensitive queries. To balance privacy and utility, a series of ligh...
arXiv:2606. 18782v1 Announce Type: cross Abstract: Large Language Models are increasingly applied to sensitive domains that require redaction of personally identifiable information (PII).
The paper introduces Veil-then-Reveal (VtR), a user-side framework that pseudo‑anonymizes personally identifiable information (PII) in prompts sent to large language models (LLMs) under a zero‑trust model. VtR replaces detected PII with realistic, same‑type surrogates using format‑preserving encryption for structured data and a keyed‑hash dictionary for semantic data, while preserving linguistic and structural cues. The system achieves high detection recall (97.9%), strong protection of detected values (95.2%), and near‑perfect round‑trip restoration (99.9%) across three datasets.
The paper presents a method for extracting key information from OCR‑digitized clinical reports, addressing challenges posed by heterogeneous documents and noisy OCR output. It introduces an open key space that is iteratively mined, normalized, clustered, and verified to build a canonical key inventory, and defines key coverage as a metric for inventory completeness. Experiments on reports from over 20 hospitals using a 0.2B BERT model show that performance improves steadily with key coverage, achieving high F1 scores when the top 90 keys are covered and outperforming a fine‑tuned Qwen3‑0.6B baseline.
The rapid spread of false and misleading health information through digital platforms has become a major public health challenge, particularly during infectious disease outbreaks where delayed verification can influence public behaviour and hinder effective disease control. Although recent advances in automated health misinformation detection have shown encouraging results, most existing approaches rely heavily on global biomedical resources and often fail to capture the local context needed to verify claims in developing countries.
The paper presents a practical private dense retrieval system that uses learned deep hashing as a private filter to generate a short candidate list for each query. Encrypted reranking and oblivious key transfer protect the exact query and final selection, allowing the system to match full‑corpus retrieval quality with only 200‑500 candidates. Experiments on five zero‑shot corpora and the 2.68M‑passage NQ corpus show minimal latency overhead and strong privacy guarantees.
arXiv:2609.15871v1 Announce Type: cross Abstract: Rising societal and lifestyle complexity has been linked to a growing prevalence of mental distress worldwide. Educational institutions, workplaces,...
FedSumUp introduces a learnable lossy compressor that generates compact, visually interpretable representations on client devices, reducing privacy leakage and computational overhead. The compressor is trained on the server using a data‑free closed‑loop optimization that minimizes the difference between original and twice‑compressed representations after upsampling. Experiments on BloodMNIST, CIFAR‑10, and ChestMNIST show low reconstruction PSNR, significant client runtime reductions, and competitive classification accuracy while remaining robust to non‑IID data.
The paper demonstrates that publicly available clinical machine learning models, such as logistic regression (LR), pose significant privacy risks because attackers can recover model parameters and identify training cohorts through various membership inference attacks. The authors show that even small cohorts can be reliably identified and that common practices like cross-validation can worsen the risk. To mitigate this, they propose a quantum-inspired defense that tensorizes discretized models into tensor trains (TTs), which obfuscates parameters, preserves accuracy, and maintains interpretability while providing black‑box protection comparable to Differential Privacy.
arXiv:2607. 19524v1 Announce Type: cross Abstract: Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted data sharing, client heterogeneity, class imbalance, and the lack of realistic tabular electronic health record (EHR) benchmarks.