Human-Centered Interactive Anonymization for Privacy-Preserving Machine Learning: a Case for Human-Guided K-Anonymity

Privacy-preserving machine learning ML seeks to balance data utility and privacy, especially as regulations like the GDPR mandate the anonymization of personal data for ML applications. Conventional anonymization approaches often reduce data utility due to indiscriminate generalization or...

Willchain: Decentralized, Privacy-Preserving, Self-Executing, Digital Wills

This work presents a novel decentralized protocol for digital estate planning that integrates advances distributed computing, and cryptography. The original proof-of-concept was constructed using purely solidity contracts. Since then, we have enhanced the implementation into a layer-1 protocol th...

PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection

This paper tackles the challenging and practical problem of multi-identifier private user profile matching for privacy-preserving ad measurement, a cornerstone of modern advertising analytics. We introduce a comprehensive cryptographic framework leveraging reversed Oblivious Pseudorandom Function...

ZK-SERIES: Privacy-Preserving Authentication Using Temporal Biometric Data

Biometric authentication relies on physiological or behavioral traits that are inherent to a user, making them difficult to lose, forge or forget. Biometric data with a temporal component enable the following authentication protocol: recent readings of the underlying biometrics are encoded as tim...

Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning

Whitepaper called Secure Multi-Key Homomorphic Encryption With Application To Privacy-Preserving Federated Learning...

On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification

What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classification, where statistical effects due to limited data availability play a...

Bidirectional Biometric Authentication Using Transciphering and (T)FHE

Biometric authentication systems pose privacy risks, as leaked templates such as iris or fingerprints can lead to security breaches. Fully Homomorphic Encryption FHE enables secure encrypted evaluation, but its deployment is hindered by large ciphertexts, high key overhead, and limited trust...

Anonymous Authentication using Attribute-based Encryption

In today's digital age, personal data is constantly at risk of compromise. Attribute-Based Encryption ABE has emerged as a promising approach to privacy-preserving data protection. This paper proposes an anonymous authentication mechanism based on ABE, which allows users to authenticate without...

Zero-Knowledge Proof-Of-Location Protocols for Vehicle Subsidies and Taxation Compliance

This paper introduces a new set of privacy-preserving mechanisms for verifying compliance with location-based policies for vehicle taxation, or for electric vehicle EV subsidies, using Zero-Knowledge Proofs ZKPs. We present the design and evaluation of a Zero-Knowledge Proof-of-Location ZK-PoL...

Emission Impossible: Privacy-Preserving Carbon Emissions Claims

Information and Communication Technologies ICT have a significant climate impact, and data centres account for a large proportion of the carbon emissions from ICT. To achieve sustainability goals, it is important that all parties involved in ICT supply chains can track and share accurate carbon...

PYSEC-2025-221

vantage6 is an open-source infrastructure for privacy preserving analysis. The JWT secret key in the vantage6 server is auto-generated unless defined by the user. The auto-generated key is a UUID1, which is not cryptographically secure as it is predictable to some extent. This vulnerability is...

Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR

Extended reality XR systems, which consist of virtual reality VR, augmented reality AR, and mixed reality XR, offer a transformative interface for immersive, multi-modal, and embodied human-computer interaction. In this paper, we envision that multi-modal multi-task M3T federated foundation model...

vantage6 安全漏洞

vantage6 is a vantage6 open source priVAcy preserviNg federalTed leArningG infrastructure for Secure Insight eXchange. A security vulnerability exists in vantage6 versions prior to 4.11 that stems from the change password feature allowing unlimited attempts, which could lead to a brute force atta...

Are Trees Really Green? A Detection Approach of IoT Malware Attacks

Nowadays, the Internet of Things IoT is widely employed, and its usage is growing exponentially because it facilitates remote monitoring, predictive maintenance, and data-driven decision making, especially in the healthcare and industrial sectors. However, IoT devices remain vulnerable due to the...

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...

Secure Distributed Learning for CAVs: Defending against Gradient Leakage with Leveled Homomorphic Encryption

Federated Learning FL enables collaborative model training across distributed clients without sharing raw data, making it a promising approach for privacy-preserving machine learning in domains like Connected and Autonomous Vehicles CAVs. However, recent studies have shown that exchanged model...

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...