Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing

Split Learning SL -- splits a model into two distinct parts to help protect client data while enhancing Machine Learning ML processes. Though promising, SL has proven vulnerable to different attacks, thus raising concerns about how effective it may be in terms of data privacy. Recent works have...

CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories

Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System GPS data, which is susceptible to GPS data outage. This paper...

Interpreting Differential Privacy in Terms of Disclosure Risk

As the use of differential privacy DP becomes widespread, the development of effective tools for reasoning about the privacy guarantee becomes increasingly critical. In pursuit of this goal, we demonstrate novel relationships between DP and measures of statistical disclosure risk. We suggest how...

SmartphoneDemocracy: Privacy-Preserving E-Voting on Decentralized Infrastructure Using Novel European Identity

The digitization of democratic processes promises greater accessibility but presents challenges in terms of security, privacy, and verifiability. Existing electronic voting systems often rely on centralized architectures, creating single points of failure and forcing too much trust in authorities...

New Study Shows Google Tracking Persists Even With Privacy Tools

A new SafetyDetectives study reveals the surprising extent of Google tracking across the web in the US, UK, Switzerland, and Sweden. Discover how Google Analytics, AdSense, and YouTube embeds collect your data, even when using DuckDuckGo...

Quantum-Resilient Privacy Ledger (QRPL): a Sovereign Digital Currency for the Post-Quantum Era

The emergence of quantum computing presents profound challenges to existing cryptographic infrastructures, whilst the development of central bank digital currencies CBDCs has raised concerns regarding privacy preservation and excessive centralisation in digital payment systems. This paper propose...

Entangled Threats: a Unified Kill Chain Model for Quantum Machine Learning Security

Quantum Machine Learning QML systems inherit vulnerabilities from classical machine learning while introducing new attack surfaces rooted in the physical and algorithmic layers of quantum computing. Despite a growing body of research on individual attack vectors - ranging from adversarial poisoni...

RADAR: a Radio-Based Analytics for Dynamic Association and Recognition of Pseudonyms in VANETs

This paper presents RADAR, a tracking algorithm for vehicles participating in Cooperative Intelligent Transportation Systems C-ITS that exploits multiple radio signals emitted by a modern vehicle to break privacy-preserving pseudonym schemes deployed in VANETs. This study shows that by combining...

Beyond the Worst Case: Extending Differential Privacy Guarantees to Realistic Adversaries

Differential Privacy DP is a family of definitions that bound the worst-case privacy leakage of a mechanism. One important feature of the worst-case DP guarantee is it naturally implies protections against adversaries with less prior information, more sophisticated attack goals, and complex...

Towards Privacy-Preserving and Personalized Smart Homes Via Tailored Small Language Models

Large Language Models LLMs have showcased remarkable generalizability in language comprehension and hold significant potential to revolutionize human-computer interaction in smart homes. Existing LLM-based smart home assistants typically transmit user commands, along with user profiles and home...

Temporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking

With the rise of social media, vast amounts of user-uploaded videos e.g., YouTube are utilized as training data for Visual Object Tracking VOT. However, the VOT community has largely overlooked video data-privacy issues, as many private videos have been collected and used for training commercial...

SUSE-SU-2025:02259-1 Recommended update for gpg2

This update for gpg2 fixes the following issues: - CVE-2025-30258: Fixed DoS due to a malicious subkey in the keyring bsc1239119. Other bugfixes: - Do not install expired sks certificate bsc1243069. - gpg hangs when importing a key bsc1236931...

Yet Another Strava Privacy Leak

This time it's the Swedish prime minister's bodyguards. Last year, it was the US Secret Service and Emmanuel Macron's bodyguards. in 2018, it was secret US military bases. This is ridiculous. Why do people continue to make their data public?...

Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy

Differentially private DP mechanisms are difficult to interpret and calibrate because existing methods for mapping standard privacy parameters to concrete privacy risks -- re-identification, attribute inference, and data reconstruction -- are both overly pessimistic and inconsistent. In this work...

AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing

Federated Learning FL faces inherent challenges in balancing model performance, privacy preservation, and communication efficiency, especially in non-IID decentralized environments. Recent approaches either sacrifice formal privacy guarantees, incur high overheads, or overlook quantum-enhanced...

WatchWitch: Interoperability, Privacy, and Autonomy for the Apple Watch

Smartwatches such as the Apple Watch collect vast amounts of intimate health and fitness data as we wear them. Users have little choice regarding how this data is processed: The Apple Watch can only be used with Apple's iPhones, using their software and their cloud services. We are the first to...

Clio-X: AWeb3 Solution for Privacy-Preserving AI Access to Digital Archives

As archives turn to artificial intelligence to manage growing volumes of digital records, privacy risks inherent in current AI data practices raise critical concerns about data sovereignty and ethical accountability. This paper explores how privacy-enhancing technologies PETs and Web3 architectur...

The vulnerability of the Akamai CloudTest performance testing platform lies in the improper limitation of XML links to external objects, which allows attackers to compromise privacy.

The vulnerability of the Akamai CloudTest performance testing platform relates to incorrect restrictions on XML links to external objects. Exploiting this vulnerability could allow a malicious actor to compromise privacy...

Privacy-Utility-Fairness: a Balanced Approach to Vehicular-Traffic Management System

Location-based vehicular traffic management faces significant challenges in protecting sensitive geographical data while maintaining utility for traffic management and fairness across regions. Existing state-of-the-art solutions often fail to meet the required level of protection against linkage...

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning

As IoT ecosystems continue to expand across critical sectors, they have become prominent targets for increasingly sophisticated and large-scale malware attacks. The evolving threat landscape, combined with the sensitive nature of IoT-generated data, demands detection frameworks that are both...