arXiv AI August 18, 2026 By Sourya Joyee De, Abdessamad Imine

A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemes

arXiv:2608. 16461v1 Announce Type: cross Abstract: Central Bank Digital Currency (CBDC)-based welfare schemes may be potentially privacy invasive as they process significant volumes of beneficiary personal data and lead to privacy harms such as surveillance, discrimination and stigmatization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language

Aug 31

ProfileFoundry: A Synthetic Person-Object Substrate for Privacy, Memory, and Tool-Use Evaluation in LLM Agent

arXiv:2606.26403v2 Announce Type: replace Abstract: Foundation-model research increasingly needs data about people: user state, personal histories, relationships, contact-like fields, documents, and...

By Sriram Selvam, Anneswa Ghosh

llms agents safety

arXiv AI

4d ago

PrivacySkills: How Privacy Guidance Shapes Source Selection in LLM Agents

arXiv:2609.35937v1 Announce Type: cross Abstract: While prior work has documented privacy failures in LLM agents, it remains unclear how the presentation of privacy guidance influences their choice o...

By Lucas Biechy, C\'edric Eichler, H\'eber H. Arcolezi, Nicolas Anciaux

llms agents safety

arXiv AI

Jun 30

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks

arXiv:2504. 17421v2 Announce Type: replace-cross Abstract: Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-specific tasks; small models (SMs), in contrast, are more efficient and tailored to specific domains yet lack general-purpose coverage.

By Yang Liu, Kejia Zhang, Bingjie Yan, Tianyuan Zou, Jianqing Zhang, Zixuan Gu, Xiangsen Chen, Jianbing Ding, Xidong Wang, Jingyi Li, Xiaozhou Ye, Ye Ouyang, Qiang Yang, Ya-Qin Zhang

llms fine-tuning safety

arXiv AI

Jun 10

IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts

arXiv:2606. 09908v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challenge for their design and evaluation.

By Ayana Hussain, Soumya Sharma, Golnoosh Farnadi, Nicholas Vincent, H\'eber Hwang Arcolezi, Ulrich A\"ivodji

llms benchmarks safety

arXiv Machine Learning

Jun 18

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

arXiv:2502. 17748v4 Announce Type: replace Abstract: Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks.

By Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

safety

arXiv AI

Jun 19

FFinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming

arXiv:2606. 19887v1 Announce Type: cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.

By Chaeyun Kim, Daeyoung Park, Junghwan Kim, Jinyoung Jeong, Eunji Song, Yongtaek Lim, Minwoo Kim

llms benchmarks safety

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Related stories

ProfileFoundry: A Synthetic Person-Object Substrate for Privacy, Memory, and Tool-Use Evaluation in LLM Agent

PrivacySkills: How Privacy Guidance Shapes Source Selection in LLM Agents

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks

IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

FFinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming