List-Decodable Byzantine Robust PIR: Lower Communication Complexity, Higher Byzantine Tolerance, Smaller List Size

Private Information Retrieval PIR is a privacy-preserving primitive in cryptography. Significant endeavors have been made to address the variant of PIR concerning the malicious servers. Among those endeavors, list-decodable Byzantine robust PIR schemes may tolerate a majority of malicious...

FARFETCH'D: a Side-Channel Analysis Framework for Privacy Applications on Confidential Virtual Machines

Confidential virtual machines CVMs based on trusted execution environments TEEs enable new privacy-preserving solutions. Yet, they leave side-channel leakage outside their threat model, shifting the responsibility of mitigating such attacks to developers. However, mitigations are either not gener...

Locally Differentially Private Frequency Estimation Via Joint Randomized Response

Local Differential Privacy LDP has been widely recognized as a powerful tool for providing a strong theoretical guarantee of data privacy to data contributors against an untrusted data collector. Under a typical LDP scheme, each data contributor independently randomly perturbs their data before...

Optimal Piecewise-Based Mechanism for Collecting Bounded Numerical Data under Local Differential Privacy

Numerical data with bounded domains is a common data type in personal devices, such as wearable sensors. While the collection of such data is essential for third-party platforms, it raises significant privacy concerns. Local differential privacy LDP has been shown as a framework providing provabl...

One Patch to Rule Them All: Transforming Static Patches into Dynamic Attacks in the Physical World

Numerous methods have been proposed to generate physical adversarial patches PAPs against real-world machine learning systems. However, each existing PAP typically supports only a single, fixed attack goal, and switching to a different objective requires re-generating and re-deploying a new PAP...

Attacking Attention of Foundation Models Disrupts Downstream Tasks

Foundation models represent the most prominent and recent paradigm shift in artificial intelligence. Foundation models are large models, trained on broad data that deliver high accuracy in many downstream tasks, often without fine-tuning. For this reason, models such as CLIP , DINO or Vision...

Rewriting the Budget: a General Framework for Black-Box Attacks under Cost Asymmetry

Traditional decision-based black-box adversarial attacks on image classifiers aim to generate adversarial examples by slightly modifying input images while keeping the number of queries low, where each query involves sending an input to the model and observing its output. Most existing methods...

Cyber Security of Sensor Systems for State Sequence Estimation: an AI Approach

Sensor systems are extremely popular today and vulnerable to sensor data attacks. Due to possible devastating consequences, counteracting sensor data attacks is an extremely important topic, which has not seen sufficient study. This paper develops the first methods that accurately...

Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation

Achieving differentially private computations in decentralized settings poses significant challenges, particularly regarding accuracy, communication cost, and robustness against information leakage. While cryptographic solutions offer promise, they often suffer from high communication overhead or...

Efficient Preimage Approximation for Neural Network Certification

The growing reliance on artificial intelligence in safety- and security-critical applications demands effective neural network certification. A challenging real-world use case is certification against patch attacks'', where adversarial patches or lighting conditions obscure parts of images, for...

PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects

The average treatment effect ATE is widely used to evaluate the effectiveness of drugs and other medical interventions. In safety-critical applications like medicine, reliable inferences about the ATE typically require valid uncertainty quantification, such as through confidence intervals CIs...

CVE-2024-32509

Missing Authorization vulnerability in Loopus WP Cost Estimation & Payment Forms Builder.This issue affects WP Cost Estimation & Payment Forms Builder: from n/a through 10.1.76...

CVE-2020-15350

RIOT 2020.04 has a buffer overflow in the base64 decoder. The decoding function base64decode uses an output buffer estimation function to compute the required buffer capacity and validate against the provided buffer size. The base64estimatedecodesize function calculates the expected decoded size...

Verifying Differentially Private Median Estimation

Differential Privacy DP is a robust privacy guarantee that is widely employed in private data analysis today, finding broad application in domains such as statistical query release and machine learning. However, DP achieves privacy by introducing noise into data or query answers, which malicious...

Real-Time Detection of Insider Threats Using Behavioral Analytics and Deep Evidential Clustering

Insider threats represent one of the most critical challenges in modern cybersecurity. These threats arise from individuals within an organization who misuse their legitimate access to harm the organization's assets, data, or operations. Traditional security mechanisms, primarily designed for...

Trustworthy Reputation Games and Applications to Proof-Of-Reputation Blockchains

Reputation systems play an essential role in the Internet era, as they enable people to decide whom to trust, by collecting and aggregating data about users' behavior. Recently, several works proposed the use of reputation for the design and scalability improvement of decentralized blockchain...

BeamClean: Language Aware Embedding Reconstruction

In this work, we consider an inversion attack on the obfuscated input embeddings sent to a language model on a server, where the adversary has no access to the language model or the obfuscation mechanism and sees only the obfuscated embeddings along with the model's embedding table. We propose...

Private Statistical Estimation Via Truncation

We introduce a novel framework for differentially private DP statistical estimation via data truncation, addressing a key challenge in DP estimation when the data support is unbounded. Traditional approaches rely on problem-specific sensitivity analysis, limiting their applicability. By leveragin...

NCorr-FP: a Neighbourhood-Based Correlation-Preserving Fingerprinting Scheme for Intellectual Property Protection of Structured Data

Ensuring data ownership and traceability of unauthorised redistribution are central to safeguarding intellectual property in shared data environments. Data fingerprinting addresses these challenges by embedding recipient-specific marks into the data, typically via content modifications. We propos...

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks

The loss landscape of Variational Quantum Neural Networks VQNNs is characterized by local minima that grow exponentially with increasing qubits. Because of this, it is more challenging to recover information from model gradients during training compared to classical Neural Networks NNs. In this...