Latest Articles for Data Security

New methods improve model robustness against adversarial attacks using tabular data.

QRNGs use quantum mechanics for true randomness, vital for secure digital communication.

Exploring the integration of TEEs and security coprocessors for better data protection.

Learn methods to safeguard PII while using Large Language Models.

A new method for safeguarding generative models against physical and functional theft.

FedSMOO enhances federated learning by addressing data diversity and communication issues.

A look at methods to secure and profit from AI prompts.

A new method certifies neural networks' predictions under various image changes.

New methods enhance encryption efficiency for machine learning applications.

A new method improves neural network defenses against adversarial attacks.

Exploring strategies to enhance machine learning models against harmful data attacks.

New framework improves trust in IoT data by authenticating devices via hardware performance.

A look into the importance of securing sensitive data in cloud storage.

A new framework integrates privacy and robustness techniques for trustworthy machine learning.

This article examines privacy issues in embodied AI through gradient inversion methods.

Mobile apps often lack clarity in data collection, impacting user trust.

This article examines client-side scanning and its implications for privacy and security.

Machine learning techniques are reshaping cryptographic approaches to Learning With Errors.

Learn how DAT aids in efficient data management.

Explore how quantum pseudorandom generators enhance security in computing and cryptography.

NMDS matrices balance security and efficiency, essential for lightweight cryptographic systems.

New methods to assess privacy risks in social networks, considering distant connections.

A fresh look at speaker anonymization and the crucial role of vocoders.

Examining how randomness impacts machine learning vulnerabilities and the need for better standards.

Examining the balance between privacy and fairness in federated learning models.

Exploring advancements in searchable encryption and homomorphic encryption for data privacy.

A novel detection method for adversarial samples without needing original training data.

This paper addresses the challenges of fake reviews in recommendation systems.

Efficiently process sensitive time series data in the cloud securely.

BLEND combines secure storage and communication for efficient IoT data management.

RowPress poses new risks to data safety in modern DRAM systems.

Combining federated learning and blockchain enhances data privacy in collaborative models.

Learn about securing power-line communication against eavesdropping.

This article discusses methods to safeguard privacy in machine learning by modifying text data.

Exploring the importance of XAI in enhancing O-RAN network efficiency.

A new framework combines federated learning and blockchain to enhance data protection.

A tool to check personal information safety in large language models.

Examine the risks and defenses against power side-channel attacks on cloud FPGAs.

Study uncovers critical weaknesses in generative autoencoders regarding adversarial attacks.

CloudSec simplifies the management of security policies for cloud services.