A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption

Federated Learning FL enables collaborative model training without sharing raw data, making it a promising approach for privacy-sensitive domains. Despite its potential, FL faces significant challenges, particularly in terms of communication overhead and data privacy. Privacy-preserving Technique...

Privacy-Preserving Drone Navigation through Homomorphic Encryption for Collision Avoidance

As drones increasingly deliver packages in neighborhoods, concerns about collisions arise. One solution is to share flight paths within a specific zip code, but this compromises business privacy by revealing delivery routes. For example, it could disclose which stores send packages to certain...

Meta execs pay the pain away with $8 billion privacy settlement

Meta chief Mark Zuckerberg and several other members of the social media giant's top brass agreed to settle increasingly heated privacy violation claims for the price of $8 billion. It is far from the first time that the company, its subsidiary Facebook, or its executives have responded to allege...

Stablecoins: Fundamentals, Emerging Issues, and Open Challenges

Stablecoins, with a capitalization exceeding 200 billion USD as of January 2025, have shown significant growth, with annual transaction volumes exceeding 10 trillion dollars in 2023 and nearly doubling that figure in 2024. This exceptional success has attracted the attention of traditional...

WeTransfer walks back clause that said it would train AI on your files

File sharing site WeTransfer has rolled back language that allowed it to train machine learning models on any files that its users uploaded. The change was made after criticisms from its users. The company had quietly inserted the new language in the terms and conditions on its website. Sometime...

Security Vulnerabilities in ICEBlock

The ICEBlock tool has vulnerabilities: The developer of ICEBlock, an iOS app for anonymously reporting sightings of US Immigration and Customs Enforcement ICE officials, promises that it "ensures user privacy by storing no personal data." But that claim has come under scrutiny. ICEBlock creator...

IDFace: Face Template Protection for Efficient and Secure Identification

As face recognition systems FRS become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be recovered from the template. Although recent advances in cryptographic tools such...

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy

To mitigate privacy leakage and performance issues in personalized advertising, this paper proposes a framework that integrates federated learning and differential privacy. The system combines distributed feature extraction, dynamic privacy budget allocation, and robust model aggregation to balan...

Unveiling Usability Challenges in Web Privacy Controls

With the increasing concerns around privacy and the enforcement of data privacy laws, many websites now provide users with privacy controls. However, locating these controls can be challenging, as they are frequently hidden within multiple settings and layers. Moreover, the lack of standardizatio...

How Secure Is Online Fax: Privacy and Data Protection Standards

When it comes to sharing sensitive documents online, security sits at the top of everyone’s checklist. Online faxing is…...

FacialMotionID: Identifying Users of Mixed Reality Headsets Using Abstract Facial Motion Representations

Facial motion capture in mixed reality headsets enables real-time avatar animation, allowing users to convey non-verbal cues during virtual interactions. However, as facial motion data constitutes a behavioral biometric, its use raises novel privacy concerns. With mixed reality systems becoming...

A Review of Privacy Metrics for Privacy-Preserving Synthetic Data Generation

Privacy Preserving Synthetic Data Generation PP-SDG has emerged to produce synthetic datasets from personal data while maintaining privacy and utility. Differential privacy DP is the property of a PP-SDG mechanism that establishes how protected individuals are when sharing their sensitive data. I...

Dedicated Proxies: A Key Tool for Online Privacy, Security and Speed

Online privacy, security, and performance today are more important than ever. For professionals and businesses working online, it’s…...

The Man behind the Sound: Demystifying Audio Private Attribute Profiling Via Multimodal Large Language Model Agents

Our research uncovers a novel privacy risk associated with multimodal large language models MLLMs: the ability to infer sensitive personal attributes from audio data -- a technique we term audio private attribute profiling. This capability poses a significant threat, as audio can be covertly...

Crypto-Assisted Graph Degree Sequence Release under Local Differential Privacy

Whitepaper called Crypto-Assisted Graph Degree Sequence Release Under Local Differential Privacy...

Optimal Debiased Inference on Privatized Data Via Indirect Estimation and Parametric Bootstrap

We design a debiased parametric bootstrap framework for statistical inference from differentially private data. Existing usage of the parametric bootstrap on privatized data ignored or avoided handling the effect of clamping, a technique employed by the majority of privacy mechanisms. Ignoring th...

SynthGuard: Redefining Synthetic Data Generation with a Scalable and Privacy-Preserving Workflow Framework

The growing reliance on data-driven applications in sectors such as healthcare, finance, and law enforcement underscores the need for secure, privacy-preserving, and scalable mechanisms for data generation and sharing. Synthetic data generation SDG has emerged as a promising approach but often...

Exploring User Security and Privacy Attitudes and Concerns toward the Use of General-Purpose LLM Chatbots for Mental Health

Individuals are increasingly relying on large language model LLM-enabled conversational agents for emotional support. While prior research has examined privacy and security issues in chatbots specifically designed for mental health purposes, these chatbots are overwhelmingly "rule-based" offering...

Differentially Private Federated Low Rank Adaptation beyond Fixed-Matrix

Large language models LLMs typically require fine-tuning for domain-specific tasks, and LoRA offers a computationally efficient approach by training low-rank adapters. LoRA is also communication-efficient for federated LLMs when multiple users collaboratively fine-tune a global LLM model without...

"Is It Always Watching? Is It Always Listening?" Exploring Contextual Privacy and Security Concerns toward Domestic Social Robots

Equipped with artificial intelligence AI and advanced sensing capabilities, social robots are gaining interest among consumers in the United States. These robots seem like a natural evolution of traditional smart home devices. However, their extensive data collection capabilities, anthropomorphic...