PT-2025-20782 · Apple · Apple Macos

Name of the Vulnerable Software and Affected Versions: macOS versions prior to 15.5 Description: An information disclosure issue was addressed with improved privacy controls. This issue allows an app to potentially access sensitive user data. Recommendations: For versions prior to 15.5, update to...

PT-2025-20733 · Apple · Macos Sequoia +3

Name of the Vulnerable Software and Affected Versions: macOS Ventura versions prior to 13.7.6 macOS Sequoia versions prior to 15.5 macOS Sonoma versions prior to 14.7.6 Description: A logic issue was addressed with improved checks, allowing an app to potentially bypass certain Privacy preferences...

Apple macOS 安全漏洞

Apple macOS is a specialized operating system developed for Mac computers by Apple Inc. in the United States. A security vulnerability exists in Apple macOS that stems from a privacy issue and could lead to applications accessing sensitive user data...

PT-2025-20774 · Apple · Ipados +1

Name of the Vulnerable Software and Affected Versions: iOS versions prior to 18.5 iPadOS versions prior to 18.5 Description: A privacy issue was addressed by removing sensitive data. Call history from deleted apps may still appear in spotlight search results. Recommendations: For iOS versions pri...

PT-2025-20788 · Apple · Ipados +4

Name of the Vulnerable Software and Affected Versions: iPadOS versions prior to 17.7.7 macOS Ventura versions prior to 13.7.6 macOS Sequoia versions prior to 15.5 macOS Sonoma versions prior to 14.7.6 Description: A privacy issue was addressed with improved private data redaction for log entries...

PT-2025-20741 · Apple · Macos Sonoma +3

Name of the Vulnerable Software and Affected Versions: macOS Ventura versions prior to 13.7.6 macOS Sequoia versions prior to 15.5 macOS Sonoma versions prior to 14.7.6 Description: A privacy issue was addressed with improved private data redaction for log entries, which may have allowed an app t...

Security of Internet of Agents: Attacks and Countermeasures

With the rise of large language and vision-language models, AI agents have evolved into autonomous, interactive systems capable of perception, reasoning, and decision-making. As they proliferate across virtual and physical domains, the Internet of Agents IoA has emerged as a key infrastructure fo...

Source Anonymity for Private Random Walk Decentralized Learning

This paper considers random walk-based decentralized learning, where at each iteration of the learning process, one user updates the model and sends it to a randomly chosen neighbor until a convergence criterion is met. Preserving data privacy is a central concern and open problem in decentralize...

Securing Genomic Data against Inference Attacks in Federated Learning Environments

Federated Learning FL offers a promising framework for collaboratively training machine learning models across decentralized genomic datasets without direct data sharing. While this approach preserves data locality, it remains susceptible to sophisticated inference attacks that can compromise...

DP-TRAE: a Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protection

In the field of digital security, Reversible Adversarial Examples RAE combine adversarial attacks with reversible data hiding techniques to effectively protect sensitive data and prevent unauthorized analysis by malicious Deep Neural Networks DNNs. However, existing RAE techniques primarily focus...

Google Pays $1.375 Billion to Texas Over Unauthorized Tracking and Biometric Data Collection

Google has agreed to pay the U.S. state of Texas nearly $1.4 billion to settle two lawsuits that accused the company of tracking users' personal location and maintaining their facial recognition data without consent. The $1.375 billion payment dwarfs the fines the tech giant has paid to settle...

Privacy-Aware Berrut Approximated Coded Computing Applied to General Distributed Learning

Coded computing is one of the techniques that can be used for privacy protection in Federated Learning. However, most of the constructions used for coded computing work only under the assumption that the computations involved are exact, generally restricted to special classes of functions, and...

A Contrastive Federated Semi-Supervised Learning Intrusion Detection Framework for Internet of Robotic Things

In intelligent industry, autonomous driving and other environments, the Internet of Things IoT highly integrated with robotic to form the Internet of Robotic Things IoRT. However, network intrusion to IoRT can lead to data leakage, service interruption in IoRT and even physical damage by...

An \Tilde{O}Ptimal Differentially Private Learner for Concept Classes with VC Dimension 1

We present the first nearly optimal differentially private PAC learner for any concept class with VC dimension 1 and Littlestone dimension $d$. Our algorithm achieves the sample complexity of $\tildeO\varepsilon,δ,α,δ\log^ d$, nearly matching the lower bound of $Ω\log^ d$ proved by Alon et al...

DPolicy: Managing Privacy Risks across Multiple Releases with Differential Privacy

Differential Privacy DP has emerged as a robust framework for privacy-preserving data releases and has been successfully applied in high-profile cases, such as the 2020 US Census. However, in organizational settings, the use of DP remains largely confined to isolated data releases. This approach...

Self-Supervised Federated GNSS Spoofing Detection with Opportunistic Data

Global navigation satellite systems GNSS are vulnerable to spoofing attacks, with adversarial signals manipulating the location or time information of receivers, potentially causing severe disruptions. The task of discerning the spoofing signals from benign ones is naturally relevant for machine...

Privacy-Preserving Credit Card Approval Using Homomorphic SVM: toward Secure Inference in FinTech Applications

The growing use of machine learning in cloud environments raises critical concerns about data security and privacy, especially in finance. Fully Homomorphic Encryption FHE offers a solution by enabling computations on encrypted data, but its high computational cost limits practicality. In this...

A Taxonomy of Attacks and Defenses in Split Learning

Split Learning SL has emerged as a promising paradigm for distributed deep learning, allowing resource-constrained clients to offload portions of their model computation to servers while maintaining collaborative learning. However, recent research has demonstrated that SL remains vulnerable to a...

Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning

Functional encryption FE has recently attracted interest in privacy-preserving machine learning PPML for its unique ability to compute specific functions on encrypted data. A related line of work focuses on noisy FE, which ensures differential privacy in the output while keeping the data encrypte...

RiM: Record, Improve and Maintain Physical Well-Being Using Federated Learning

In academic settings, the demanding environment often forces students to prioritize academic performance over their physical well-being. Moreover, privacy concerns and the inherent risk of data breaches hinder the deployment of traditional machine learning techniques for addressing these health...