IDCloak: a Practical Secure Multi-Party Dataset Join Framework for Vertical Privacy-Preserving Machine Learning

Vertical privacy-preserving machine learning vPPML enables multiple parties to train models on their vertically distributed datasets while keeping datasets private. In vPPML, it is critical to perform the secure dataset join, which aligns features corresponding to intersection IDs across datasets...

CVE-2025-3913

Mattermost versions 10.7.x = 10.7.0, 10.6.x = 10.6.2, 10.5.x = 10.5.3, 9.11.x = 9.11.12 fail to properly validate permissions when changing team privacy settings, allowing team administrators without the 'invite user' permission to access and modify team invite IDs via the...

Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment

Edge Intelligence EI serves as a critical enabler for privacy-preserving systems by providing AI-empowered computation and distributed caching services at the edge, thereby minimizing latency and enhancing data privacy. The integration of blockchain technology further augments EI frameworks by...

Browser Fingerprinting Using WebAssembly

Web client fingerprinting has become a widely used technique for uniquely identifying users, browsers, operating systems, and devices with high accuracy. While it is beneficial for applications such as fraud detection and personalized experiences, it also raises privacy concerns by enabling...

Unlearning Inversion Attacks for Graph Neural Networks

Graph unlearning methods aim to efficiently remove the impact of sensitive data from trained GNNs without full retraining, assuming that deleted information cannot be recovered. In this work, we challenge this assumption by introducing the graph unlearning inversion attack: given only black-box...

Dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation

We propose dpmm, an open-source library for synthetic data generation with Differentially Private DP guarantees. It includes three popular marginal models -- PrivBayes, MST, and AIM -- that achieve superior utility and offer richer functionality compared to alternative implementations...

Breaking the Gold Standard: Extracting Forgotten Data under Exact Unlearning in Large Language Models

Large language models are typically trained on datasets collected from the web, which may inadvertently contain harmful or sensitive personal information. To address growing privacy concerns, unlearning methods have been proposed to remove the influence of specific data from trained models. Of...

Local Frames: Exploiting Inherited Origins to Bypass Content Blockers

We present a study of how local frames i.e., iframes with non-URL sources like "about:blank" are mishandled by a wide range of popular Web security and privacy tools. As a result, users of these tools remain vulnerable to the very attack techniques they seek to protect against, including browser...

Next Generation Authentication for Data Spaces: an Authentication Flow Based on Grant Negotiation and Authorization Protocol for Verifiable Presentations (GNAP4VP)

Identity verification in Data Spaces is a fundamental aspect of ensuring security and privacy in digital environments. This paper presents an identity verification protocol tailored for shared data environments within Data Spaces. This protocol extends the Grant Negotiation and Authorization...

Transaction Proximity: a Graph-Based Approach to Blockchain Fraud Prevention

This paper introduces a fraud-deterrent access validation system for public blockchains, leveraging two complementary concepts: "Transaction Proximity", which measures the distance between wallets in the transaction graph, and "Easily Attainable Identities EAIs", wallets with direct transaction...

Randextract: a Reference Library to Test and Validate Privacy Amplification Implementations

Quantum cryptographic protocols do not rely only on quantum-physical resources, they also require reliable classical communication and computation. In particular, the secrecy of any quantum key distribution protocol critically depends on the correct execution of the privacy amplification step. Th...

Adaptive Privacy-Preserving SSD

Data remanence in NAND flash complicates complete deletion on IoT SSDs. We design an adaptive architecture offering four privacy levels PL0-PL3 that select among address, data, and parity deletion techniques. Quantitative analysis balances efficacy, latency, endurance, and cost. Machine-learning...

CVE-2025-47288

Affected product: Discourse Policy plugin. Vulnerable: versions prior to 0.1.1. Root cause: a policy posted to a public topic that was tied to a private group could cause group members to be visible to non-group members. Impact: information disclosure of private-group membership (partial confiden...

Mattermost improperly allows team administrators to modify team invites

Mattermost versions 10.7.x = 10.7.0, 10.6.x = 10.6.2, 10.5.x = 10.5.3, 9.11.x = 9.11.12 fail to properly validate permissions when changing team privacy settings, allowing team administrators without the 'invite user' permission to access and modify team invite IDs via the...

CVE-2025-3913

Mattermost versions 10.7.x = 10.7.0, 10.6.x = 10.6.2, 10.5.x = 10.5.3, 9.11.x = 9.11.12 fail to properly validate permissions when changing team privacy settings, allowing team administrators without the 'invite user' permission to access and modify team invite IDs via the...

CVE-2025-3913 Team Privacy Settings Authorization Bypass in Mattermost Server

Mattermost versions 10.7.x = 10.7.0, 10.6.x = 10.6.2, 10.5.x = 10.5.3, 9.11.x = 9.11.12 fail to properly validate permissions when changing team privacy settings, allowing team administrators without the 'invite user' permission to access and modify team invite IDs via the...

CVE-2025-3913 Team Privacy Settings Authorization Bypass in Mattermost Server

Mattermost versions 10.7.x = 10.7.0, 10.6.x = 10.6.2, 10.5.x = 10.5.3, 9.11.x = 9.11.12 fail to properly validate permissions when changing team privacy settings, allowing team administrators without the 'invite user' permission to access and modify team invite IDs via the...

CVE-2025-3913 Team Privacy Settings Authorization Bypass in Mattermost Server

Mattermost versions 10.7.x = 10.7.0, 10.6.x = 10.6.2, 10.5.x = 10.5.3, 9.11.x = 9.11.12 fail to properly validate permissions when changing team privacy settings, allowing team administrators without the 'invite user' permission to access and modify team invite IDs via the...

CVE-2025-3913

Mattermost Server vulnerability CVE-2025-3913 affects versions 10.7.x <= 10.7.0, 10.6.x <= 10.6.2, 10.5.x <= 10.5.3, and 9.11.x

Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention

Cautious predictions -- where a machine learning model abstains when uncertain -- are crucial for limiting harmful errors in safety-critical applications. In this work, we identify a novel threat: a dishonest institution can exploit these mechanisms to discriminate or unjustly deny services under...