CSVAR: Enhancing Visual Privacy in Federated Learning Via Adaptive Shuffling against Overfitting

Although federated learning preserves training data within local privacy domains, the aggregated model parameters may still reveal private characteristics. This vulnerability stems from clients' limited training data, which predisposes models to overfitting. Such overfitting enables models to...

Shadow Defense against Gradient Inversion Attack in Federated Learning

Federated learning FL has emerged as a transformative framework for privacy-preserving distributed training, allowing clients to collaboratively train a global model without sharing their local data. This is especially crucial in sensitive fields like healthcare, where protecting patient data is...

Privacy-Preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models

Prompt learning is a crucial technique for adapting pre-trained multimodal language models MLLMs to user tasks. Federated prompt personalization FPP is further developed to address data heterogeneity and local overfitting, however, it exposes personalized prompts - valuable intellectual assets - ...

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning

Privacy-Preserving Federated Learning PPFL is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves privacy and security of the client's data by not exchanging it. However, ensuring that data at each client is of high quality and ready for...

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries

Federated Learning FL has emerged as a critical paradigm for enabling privacy-preserving machine learning, particularly in regulated sectors such as finance and healthcare. However, standard FL strategies often encounter significant operational challenges related to fault tolerance, system...

Zero-Trust Foundation Models: a New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things

This paper focuses on Zero-Trust Foundation Models ZTFMs, a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models FMs for Internet of Things IoT systems. By integrating core tenets, such as continuous verification, least privilege access LPA, data...

Evaluating Query Efficiency and Accuracy of Transfer Learning-Based Model Extraction Attack in Federated Learning

Federated Learning FL is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property IP threats. Model extraction ME attacks pose a significant risk to Machine Learning as a Service MLaaS platforms, enabling attackers to replicate...

Toward Malicious Clients Detection in Federated Learning

Federated learning FL enables multiple clients to collaboratively train a global machine learning model without sharing their raw data. However, the decentralized nature of FL introduces vulnerabilities, particularly to poisoning attacks, where malicious clients manipulate their local models to...

CVE-2024-21671

The vantage6 technology enables to manage and deploy privacy enhancing technologies like Federated Learning FL and Multi-Party Computation MPC. It is possible to find out usernames from the response time of login requests. This could aid attackers in credential attacks. Version 4.2.0 patches this...

CVE-2024-22193

The vantage6 technology enables to manage and deploy privacy enhancing technologies like Federated Learning FL and Multi-Party Computation MPC. There are no checks on whether the input is encrypted if a task is created in an encrypted collaboration. Therefore, a user may accidentally create a tas...

CVE-2023-28635

vantage6 is privacy preserving federated learning infrastructure. Prior to version 4.0.0, malicious users may try to get access to resources they are not allowed to see, by creating resources with integers as names. One example where this is a risk, is when users define which users are allowed to...

CVE-2023-23929

vantage6 is a privacy preserving federated learning infrastructure for secure insight exchange. Currently, the refresh token is valid indefinitely. The refresh token should get a validity of 24-48 hours. A fix was released in version 3.8.0...

CVE-2023-41882

vantage6 is privacy preserving federated learning infrastructure. The endpoint /api/collaboration/id/task is used to collect all tasks from a certain collaboration. To get such tasks, a user should have permission to view the collaboration and to view the tasks in it. However, prior to version...

CVE-2023-23930

vantage6 is privacy preserving federated learning infrastructure. Versions prior to 4.0.0 use pickle, which has known security issue, as a default serialization module but that has known security issues. All users of vantage6 that post tasks with the default serialization are affected. Version...

CVE-2023-22738

vantage6 is a privacy preserving federated learning infrastructure for secure insight exchange. Assigning existing users to a different organizations is currently possible. It may lead to unintended access: if a user from organization A is accidentally assigned to organization B, they will retain...

Privacy-Aware Cyberterrorism Network Analysis Using Graph Neural Networks and Federated Learning

Cyberterrorism poses a formidable threat to digital infrastructures, with increasing reliance on encrypted, decentralized platforms that obscure threat actor activity. To address the challenge of analyzing such adversarial networks while preserving the privacy of distributed intelligence data, we...

Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT

Industrial Internet of Things IIoT systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems IDS often rely on centralized architectures that raise concerns over data...

A Survey on Secure Machine Learning

In this survey, we will explore the interaction between secure multiparty computation and the area of machine learning. Recent advances in secure multiparty computation MPC have significantly improved its applicability in the realm of machine learning ML, offering robust solutions for...

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...

FLTG: Byzantine-Robust Federated Learning Via Angle-Based Defense and Non-IID-Aware Weighting

Byzantine attacks during model aggregation in Federated Learning FL threaten training integrity by manipulating malicious clients' updates. Existing methods struggle with limited robustness under high malicious client ratios and sensitivity to non-i.i.d. data, leading to degraded accuracy. To...