Weblate: exposure of personal IP address via email.

The exposure of personal IP addresses through email messages has been identified as a potential security issue. Email messages can pass through multiple servers, which may store or record the content, including the user's IP address, even if the email is encrypted during transit. The user's IP...

Popular Chrome Extensions Leak API Keys, User Data via HTTP and Hard-Coded Credentials

Cybersecurity researchers have flagged several popular Google Chrome extensions that have been found to transmit data in HTTP and hard-code secrets in their code, exposing users to privacy and security risks. "Several widely used extensions ... unintentionally transmit sensitive data over simple...

Pornhub, RedTube, and YouPorn block access in France, VPN use set to soar

VPNs Virtual Private Networks are suddenly popular in France. Not because France has suddenly become super privacy conscious, but because Pornhub, RedTube, and YouPorn, have blocked access in France. But why? Last year, France enacted a law mandating that pornographic sites implement stricter...

GHSA-FVX2-X7FF-FC56 Unauthenticated Disclosure of PSU HAX CMS Site Listings via haxPsuUsage API Endpoint

Summary An unauthenticated information disclosure vulnerability exists in the PSU deployment of HAX CMS via the haxPsuUsage API endpoint. This allows any remote unauthenticated user to retrieve a full list of PSU websites hosted on HAX CMS. When chained with other authorization issues e.g., HAX-3...

Breaking Anonymity at Scale: Re-Identifying the Trajectories of 100K Real Users in Japan

Mobility traces represent a critical class of personal data, often subjected to privacy-preserving transformations before public release. In this study, we analyze the anonymized Yjmob100k dataset, which captures the trajectories of 100,000 users in Japan, and demonstrate how existing anonymizati...

Inclusive, Differentially Private Federated Learning for Clinical Data

Federated Learning FL offers a promising approach for training clinical AI models without centralizing sensitive patient data. However, its real-world adoption is hindered by challenges related to privacy, resource constraints, and compliance. Existing Differential Privacy DP approaches often app...

Network Hexagons under Attack: Secure Crowdsourcing of Geo-Referenced Data

A critical requirement for modern-day Intelligent Transportation Systems ITS is the ability to collect geo-referenced data from connected vehicles and mobile devices in a safe, secure and anonymous way. The Nexagon protocol, which builds on the IETF Locator/ID Separation Protocol LISP and the...

Big Bird: Privacy Budget Management for W3C'S Privacy-Preserving Attribution API

Privacy-preserving advertising APIs like Privacy-Preserving Attribution PPA are designed to enhance web privacy while enabling effective ad measurement. PPA offers an alternative to cross-site tracking with encrypted reports governed by differential privacy DP, but current designs lack a principl...

Membership Inference Attacks on Sequence Models

Sequence models, such as Large Language Models LLMs and autoregressive image generators, have a tendency to memorize and inadvertently leak sensitive information. While this tendency has critical legal implications, existing tools are insufficient to audit the resulting risks. We hypothesize that...

FedShield-LLM: a Secure and Scalable Federated Fine-Tuned Large Language Model

Federated Learning FL offers a decentralized framework for training and fine-tuning Large Language Models LLMs by leveraging computational resources across organizations while keeping sensitive data on local devices. It addresses privacy and security concerns while navigating challenges associate...

Urania: Differentially Private Insights into AI Use

We introduce $Urania$, a novel framework for generating insights about LLM chatbot interactions with rigorous differential privacy DP guarantees. The framework employs a private clustering mechanism and innovative keyword extraction methods, including frequency-based, TF-IDF-based, and LLM-guided...

Toward a Human-Centered Evaluation Framework for Trustworthy LLM-Powered GUI Agents

The rise of Large Language Models LLMs has revolutionized Graphical User Interface GUI automation through LLM-powered GUI agents, yet their ability to process sensitive data with limited human oversight raises significant privacy and security risks. This position paper identifies three key risks ...

Privacy Amplification through Synthetic Data: Insights from Linear Regression

Synthetic data inherits the differential privacy guarantees of the model used to generate it. Additionally, synthetic data may benefit from privacy amplification when the generative model is kept hidden. While empirical studies suggest this phenomenon, a rigorous theoretical understanding is stil...

QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality

This paper introduces QA-HFL, a quality-aware hierarchical federated learning framework that efficiently handles heterogeneous image quality across resource-constrained mobile devices. Our approach trains specialized local models for different image quality levels and aggregates their features...

PCEvolve: Private Contrastive Evolution for Synthetic Dataset Generation Via Few-Shot Private Data and Generative APIs

The rise of generative APIs has fueled interest in privacy-preserving synthetic data generation. While the Private Evolution PE algorithm generates Differential Privacy DP synthetic images using diffusion model APIs, it struggles with few-shot private data due to the limitations of its DP-protect...

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification

Purpose: This study proposes a framework for fine-tuning large language models LLMs with differential privacy DP to perform multi-abnormality classification on radiology report text. By injecting calibrated noise during fine-tuning, the framework seeks to mitigate the privacy risks associated wit...

Evaluating Apple Intelligence'S Writing Tools for Privacy against Large Language Model-Based Inference Attacks: Insights from Early Datasets

The misuse of Large Language Models LLMs to infer emotions from text for malicious purposes, known as emotion inference attacks, poses a significant threat to user privacy. In this paper, we investigate the potential of Apple Intelligence's writing tools, integrated across iPhone, iPad, and...

FERRET: Private Deep Learning Faster and Better Than DPSGD

We revisit 1-bit gradient compression through the lens of mutual-information differential privacy MI-DP. Building on signSGD, we propose FERRET--Fast and Effective Restricted Release for Ethical Training--which transmits at most one sign bit per parameter group with Bernoulli masking. Theory: We...

Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation

Achieving differentially private computations in decentralized settings poses significant challenges, particularly regarding accuracy, communication cost, and robustness against information leakage. While cryptographic solutions offer promise, they often suffer from high communication overhead or...

Privacy and Security Threat for OpenAI GPTs

Large language models LLMs demonstrate powerful information handling capabilities and are widely integrated into chatbot applications. OpenAI provides a platform for developers to construct custom GPTs, extending ChatGPT's functions and integrating external services. Since its release in November...