Minoritised Ethnic People'S Security and Privacy Concerns and Responses Towards Essential Online Services

Minoritised ethnic people are marginalised in society, and therefore at a higher risk of adverse online harms, including those arising from the loss of security and privacy of personal data. Despite this, there has been very little research focused on minoritised ethnic people's security and...

SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding

Federated recommender system FedRec has emerged as a solution to protect user data through collaborative training techniques. A typical FedRec involves transmitting the full model and entire weight updates between edge devices and the server, causing significant burdens to devices with limited...

QualitEye: Public and Privacy-Preserving Gaze Data Quality Verification

Gaze-based applications are increasingly advancing with the availability of large datasets but ensuring data quality presents a substantial challenge when collecting data at scale. It further requires different parties to collaborate, therefore, privacy concerns arise. We propose QualitEye--the...

Explainer-Guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models

Binary code similarity detection BCSD serves as a fundamental technique for various software engineering tasks, e.g., vulnerability detection and classification. Attacks against such models have therefore drawn extensive attention, aiming at misleading the models to generate erroneous predictions...

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...

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...

Client-Side Zero-Shot LLM Inference for Comprehensive In-Browser URL Analysis

Malicious websites and phishing URLs pose an ever-increasing cybersecurity risk, with phishing attacks growing by 40% in a single year. Traditional detection approaches rely on machine learning classifiers or rule-based scanners operating in the cloud, but these face significant challenges in...

Keyed Chaotic Dynamics for Privacy-Preserving Neural Inference

Neural network inference typically operates on raw input data, increasing the risk of exposure during preprocessing and inference. Moreover, neural architectures lack efficient built-in mechanisms for directly authenticating input data. This work introduces a novel encryption method for ensuring...

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...

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...

CVE-2024-32969

vantage6 is an open-source infrastructure for privacy preserving analysis. Collaboration administrators can add extra organizations to their collaboration that can extend their influence. For example, organizations that they include can then create new users for which they know the passwords, and...

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-2023-41881

vantage6 is privacy preserving federated learning infrastructure. When a collaboration is deleted, the linked resources such as tasks from that collaboration should be deleted. This is partly to manage data properly, but also to prevent a potential but unlikely side-effect that affects versions...

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-2020-8276

The implementation of Brave Desktop's privacy-preserving analytics system P3A between 1.1 and 1.18.35 logged the timestamp of when the user last opened an incognito window, including Tor windows. The intended behavior was to log the timestamp for incognito windows excluding Tor windows. Note that...

LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance

Large language models LLMs are increasingly applied in fields such as finance, education, and governance due to their ability to generate human-like text and adapt to specialized tasks. However, their widespread adoption raises critical concerns about data privacy and security, including the risk...

Pura: an Efficient Privacy-Preserving Solution for Face Recognition

Face recognition is an effective technology for identifying a target person by facial images. However, sensitive facial images raises privacy concerns. Although privacy-preserving face recognition is one of potential solutions, this solution neither fully addresses the privacy concerns nor is...

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...

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...

Proof-Of-Social-Capital: Privacy-Preserving Consensus Protocol Replacing Stake for Social Capital

Consensus protocols used today in blockchains often rely on computational power or financial stakes - scarce resources. We propose a novel protocol using social capital - trust and influence from social interactions - as a non-transferable staking mechanism to ensure fairness and decentralization...