Outsourcing SAT-Based Verification Computations in Network Security

The emergence of cloud computing gives huge impact on large computations. Cloud computing platforms offer servers with large computation power to be available for customers. These servers can be used efficiently to solve problems that are complex by nature, for example, satisfiability SAT problem...

Privacy-Preserving Socialized Recommendation Based on Multi-View Clustering in a Cloud Environment

Recommendation as a service has improved the quality of our lives and plays a significant role in variant aspects. However, the preference of users may reveal some sensitive information, so that the protection of privacy is required. In this paper, we propose a privacy-preserving, socialized,...

An Efficient Private GPT Never Autoregressively Decodes

The wide deployment of the generative pre-trained transformer GPT has raised privacy concerns for both clients and servers. While cryptographic primitives can be employed for secure GPT inference to protect the privacy of both parties, they introduce considerable performance overhead.To accelerat...

CVE-2025-37979

In the Linux kernel, the following vulnerability has been resolved: ASoC: qcom: Fix sc7280 lpass potential buffer overflow Case values introduced in commit 5f78e1fb7a3e "ASoC: qcom: Add driver support for audioreach solution" cause out of bounds access in arrays of sc7280 driver data e.g. in case...

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption

Federated Learning FL is susceptible to privacy attacks, such as data reconstruction attacks, in which a semi-honest server or a malicious client infers information about other clients' datasets from their model updates or gradients. To enhance the privacy of FL, recent studies combined Multi-Key...

PT-2025-22121 · Salesforce · Omnis Studio

Name of the Vulnerable Software and Affected Versions: Salesforce OmniStudio versions prior to 254 Description: The issue is related to an Improper Preservation of Permissions vulnerability in Salesforce OmniStudio FlexCards, which allows exposure of Custom Settings data. Recommendations: For...

PT-2025-22118 · Salesforce · Omnis Studio

Name of the Vulnerable Software and Affected Versions: Salesforce OmniStudio versions prior to Spring 2025 Description: The issue is related to an Improper Preservation of Permissions vulnerability in Salesforce OmniStudio FlexCards, which allows the bypass of field level security controls for...

Outsourced Privacy-Preserving Feature Selection Based on Fully Homomorphic Encryption

Feature selection is a technique that extracts a meaningful subset from a set of features in training data. When the training data is large-scale, appropriate feature selection enables the removal of redundant features, which can improve generalization performance, accelerate the training process...

PoLO: Proof-Of-Learning and Proof-Of-Ownership at Once with Chained Watermarking

Machine learning models are increasingly shared and outsourced, raising requirements of verifying training effort Proof-of-Learning, PoL to ensure claimed performance and establishing ownership Proof-of-Ownership, PoO for transactions. When models are trained by untrusted parties, PoL and PoO mus...

Safe Delta: Consistently Preserving Safety When Fine-Tuning LLMs on Diverse Datasets

Large language models LLMs have shown great potential as general-purpose AI assistants across various domains. To fully leverage this potential in specific applications, many companies provide fine-tuning API services, enabling users to upload their own data for LLM customization. However,...

Anti-Sensing: Defense against Unauthorized Radar-Based Human Vital Sign Sensing with Physically Realizable Wearable Oscillators

Recent advancements in Ultra-Wideband UWB radar technology have enabled contactless, non-line-of-sight vital sign monitoring, making it a valuable tool for healthcare. However, UWB radar's ability to capture sensitive physiological data, even through walls, raises significant privacy concerns,...

Private Transformer Inference in MLaaS: a Survey

Transformer models have revolutionized AI, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service MLaaS raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private...

Privacy-Preserving Runtime Verification

Runtime verification offers scalable solutions to improve the safety and reliability of systems. However, systems that require verification or monitoring by a third party to ensure compliance with a specification might contain sensitive information, causing privacy concerns when usual runtime...

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data

Federated learning FL presents an effective solution for collaborative model training while maintaining data privacy across decentralized client datasets. However, data quality issues such as noisy labels, missing classes, and imbalanced distributions significantly challenge its effectiveness. Th...

Removing Watermarks with Partial Regeneration Using Semantic Information

As AI-generated imagery becomes ubiquitous, invisible watermarks have emerged as a primary line of defense for copyright and provenance. The newest watermarking schemes embed semantic signals - content-aware patterns that are designed to survive common image manipulations - yet their true...

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...

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...

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...

FedTDP: a Privacy-Preserving and Unified Framework for Trajectory Data Preparation Via Federated Learning

Trajectory data, which capture the movement patterns of people and vehicles over time and space, are crucial for applications like traffic optimization and urban planning. However, issues such as noise and incompleteness often compromise data quality, leading to inaccurate trajectory analyses and...

Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: a Scoping Review

Explainable Artificial Intelligence XAI has emerged as a pillar of Trustworthy AI and aims to bring transparency in complex models that are opaque by nature. Despite the benefits of incorporating explanations in models, an urgent need is found in addressing the privacy concerns of providing this...