NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation

Large Language Models LLM are typically trained on vast amounts of data from various sources. Even when designed modularly e.g., Mixture-of-Experts, LLMs can leak privacy on their sources. Conversely, training such models in isolation arguably prohibits generalization. To this end, we propose a...

Heavy-Tailed Privacy: the Symmetric Alpha-Stable Privacy Mechanism

With the rapid growth of digital platforms, there is increasing apprehension about how personal data is collected, stored, and used by various entities. These concerns arise from the increasing frequency of data breaches, cyber-attacks, and misuse of personal information for targeted advertising...

Protecting Your Phone—and Your Privacy—at the US Border

In this episode of Uncanny Valley, our hosts explain how to prepare for travel to and from the United States—and how to stay safe...

Blue Shield Leaked Millions of Patient Info to Google for Years

Blue Shield of California exposed the health data of 4.7 million members to Google for years due to…...

WhatsApp Adds Advanced Chat Privacy to Blocks Chat Exports and Auto-Downloads

WhatsApp has introduced an extra layer of privacy called Advanced Chat Privacy that allows users to block participants from sharing the contents of a conversation in traditional chats and groups. "This new setting available in both chats and groups helps prevent others from taking content outside...

Silenzio: Secure Non-Interactive Outsourced MLP Training

Outsourcing the ML training to cloud providers presents a compelling opportunity for resource constrained clients, while it simultaneously bears inherent privacy risks, especially for highly sensitive training data. We introduce Silenzio, the first fully non-interactive outsourcing scheme for the...

Optimizing the Privacy-Utility Balance Using Synthetic Data and Configurable Perturbation Pipelines

This paper explores the strategic use of modern synthetic data generation and advanced data perturbation techniques to enhance security, maintain analytical utility, and improve operational efficiency when managing large datasets, with a particular focus on the Banking, Financial Services, and...

From Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy

Local Differential Privacy LDP is the predominant privacy model for safeguarding individual data privacy. Existing perturbation mechanisms typically require perturbing the original values to ensure acceptable privacy, which inevitably results in value distortion and utility deterioration. In this...

Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction

With the rapid digitalization of healthcare systems, there has been a substantial increase in the generation and sharing of private health data. Safeguarding patient information is essential for maintaining consumer trust and ensuring compliance with legal data protection regulations. Machine...

CVE-2023-45720: Exposure of Private Personal Information to an Unauthorized Actor

Insufficient default configuration in HCL Leap allows anonymous access to directory information...

Shopify faces privacy lawsuit for collecting customer data

Shopify faces a data privacy class action lawsuit in the US that could change the way globally active companies can be held accountable. The proposed class action is a revival of a case that had been dismissed by a lower court judge and a three-judge 9th Circuit Court of Appeals panel. But now it...

Lattica Emerges from Stealth to Solve AI’s Biggest Privacy Challenge with FHE

Lattica’s cloud-based solution uses Fully Homomorphic Encryption to query encrypted data on AI models without decrypting it, preserving privacy and bolstering security...

Google Drops Cookie Prompt in Chrome, Adds IP Protection to Incognito

Google on Tuesday revealed that it will no longer offer a standalone prompt for third-party cookies in its Chrome browser as part of its Privacy Sandbox initiative. "We've made the decision to maintain our current approach to offering users third-party cookie choice in Chrome, and will not be...

Developing a Blockchain-Based Secure Digital Contents Distribution System

As digital content distribution expands rapidly through online platforms, securing digital media and protecting intellectual property has become increasingly complex. Traditional centralized systems, while widely adopted, suffer from vulnerabilities such as single points of failure and limited...

Private Federated Learning Using Preference-Optimized Synthetic Data

In practical settings, differentially private Federated learning DP-FL is the dominant method for training models from private, on-device client data. Recent work has suggested that DP-FL may be enhanced or outperformed by methods that use DP synthetic data Wu et al., 2024; Hou et al., 2024. The...

AiXamine: Simplified LLM Safety and Security

Evaluating Large Language Models LLMs for safety and security remains a complex task, often requiring users to navigate a fragmented landscape of ad hoc benchmarks, datasets, metrics, and reporting formats. To address this challenge, we present aiXamine, a comprehensive black-box evaluation...

CVE-2025-3645

A flaw was found in Moodle. Insufficient capability checks in a messaging web service allowed users to view other users' names and online statuses...

Quantifying Source Speaker Leakage in One-To-One Voice Conversion

Using a multi-accented corpus of parallel utterances for use with commercial speech devices, we present a case study to show that it is possible to quantify a degree of confidence about a source speaker's identity in the case of one-to-one voice conversion. Following voice conversion using a...

How Private Is Your Attention? Bridging Privacy with In-Context Learning

In-context learning ICL-the ability of transformer-based models to perform new tasks from examples provided at inference time-has emerged as a hallmark of modern language models. While recent works have investigated the mechanisms underlying ICL, its feasibility under formal privacy constraints...

Charting the Uncharted: the Landscape of Monero Peer-To-Peer Network

The Monero blockchain enables anonymous transactions through advanced cryptography in its peer-to-peer network, which underpins decentralization, security, and trustless interactions. However, privacy measures obscure peer connections, complicating network analysis. This study proposes a method t...