#privacy News & Analysis

Zcash Is Crypto’s Most Mispriced Asset, Cypherpunk CIO Says

Democrats Press Meta Over Facial Recognition Plans for Smart Glasses

Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning

Data-Local Autonomous LLM-Guided Neural Architecture Search for Multiclass Multimodal Time-Series Classification

A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education

Federated Learning with Multi-Partner OneFlorida+ Consortium Data for Predicting Major Postoperative Complications

PREFINE: Personalized Story Generation via Simulated User Critics and User-Specific Rubric Generation

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification

Evontree: Ontology Rule-Guided Self-Evolution of Large Language Models

ChangeNOW Launches Private Send to Break Blockchain Address Tracking

ChangeNOW has launched Private Send, a privacy feature integrated into NOW Wallet that prevents direct address tracking on public blockchains. The feature uses a toggle system to break the link between sender and recipient addresses during transactions.

Aster Chain Launch: Defining a New Era for Onchain Privacy and Transparency

Aster Chain has officially launched, positioning itself as a new blockchain platform focused on combining onchain privacy with transparency. The launch was announced from George Town, British Virgin Islands on March 17th, 2026, marking the platform's entry into the privacy-focused blockchain space.

FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis

Researchers propose FedUAF, a new multimodal federated learning framework that addresses challenges in sentiment analysis by using uncertainty-aware fusion and reliability-guided aggregation. The system demonstrates superior performance on benchmark datasets CMU-MOSI and CMU-MOSEI, showing improved robustness against missing modalities and unreliable client updates in federated learning environments.

FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data

Researchers propose FedPBS, a new federated learning algorithm that addresses key challenges in distributed AI training including statistical heterogeneity and uneven client participation. The algorithm dynamically adapts batch sizes and applies proximal corrections to improve model convergence while preserving data privacy across distributed clients.

The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas

Human-Centred LLM Privacy Audits: Findings and Frictions

FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification

AudioTrust: Benchmarking the Multifaceted Trustworthiness of Audio Large Language Models

Is privacy still hot? Bitcoin miner Foundry will debut a Zcash pool — here’s why

A writer is suing Grammarly for turning her and other authors into ‘AI editors’ without consent

Anthropic doesn’t trust the Pentagon, and neither should you

AI is capable of remarkable feats. And has the power to kill. Meet one woman warning about the dangers ahead

There Are No Silly Questions: Evaluation of Offline LLM Capabilities from a Turkish Perspective

A study evaluates offline large language models for Turkish heritage language education, testing 14 models from 270M to 32B parameters using a Turkish Anomaly Suite. The research finds that 8B-14B parameter reasoning-oriented models offer the best cost-safety balance for educational use, while model size alone doesn't determine anomaly resistance.

What Makes Code Generation Ethically Sourced?

Privacy by design beats regulation by reaction

The article argues that privacy-preserving decentralized technologies offer a proactive solution to regulatory concerns by addressing centralized digital risks at the design level. This approach reduces systemic vulnerabilities and vendor lock-in compared to reactive regulatory measures targeting centralized systems.

Microsoft Copilot to hijack your browser... for your own convenience

The article title suggests Microsoft Copilot will gain new browser control capabilities, framed as a convenience feature for users. However, no article body content was provided to analyze the specific details or implications of this development.