SafeTab-H: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File B (Detailed DHC-B)

This article describes SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B Detailed DHC-B as part of the 2020 Census. The tabulations contain household statistics about household type and tenure...

The DCR Delusion: Measuring the Privacy Risk of Synthetic Data

Synthetic data has become an increasingly popular way to share data without revealing sensitive information. Though Membership Inference Attacks MIAs are widely considered the gold standard for empirically assessing the privacy of a synthetic dataset, practitioners and researchers often rely on...

Think Twice Before Creating That ChatGPT Action Figure

People are using ChatGPT’s new image generator to take part in viral social media trends. But using it also puts your privacy at risk—unless you take a few simple steps to protect yourself...

Spill the Beans: Exploiting CPU Cache Side-Channels to Leak Tokens from Large Language Models

Side-channel attacks on shared hardware resources increasingly threaten confidentiality, especially with the rise of Large Language Models LLMs. In this work, we introduce Spill The Beans, a novel application of cache side-channels to leak tokens generated by an LLM. By co-locating an attack...

Can Differentially Private Fine-Tuning LLMs Protect against Privacy Attacks?

Fine-tuning large language models LLMs has become an essential strategy for adapting them to specialized tasks; however, this process introduces significant privacy challenges, as sensitive training data may be inadvertently memorized and exposed. Although differential privacy DP offers strong...

Preserving Privacy and Utility in LLM-Based Product Recommendations

Large Language Model LLM-based recommendation systems leverage powerful language models to generate personalized suggestions by processing user interactions and preferences. Unlike traditional recommendation systems that rely on structured data and collaborative filtering, LLM-based models proces...

[SECURITY] Fedora 40 Update: icecat-115.22.0-2.rh1.fc40

GNU IceCat is the GNU version of the Firefox ESR browser. Extensions included to this version of IceCat: LibreJS GNU LibreJS aims to address the JavaScript problem described in the article "The JavaScript Trap" of Richard Stallman. JShelter: Mitigates potential threats from JavaScript, including...

VDDP: Verifiable Distributed Differential Privacy under the Client-Server-Verifier Setup

Despite differential privacy DP often being considered the de facto standard for data privacy, its realization is vulnerable to unfaithful execution of its mechanisms by servers, especially in distributed settings. Specifically, servers may sample noise from incorrect distributions or generate...

Bilateral Differentially Private Vertical Federated Boosted Decision Trees

Federated learning is a distributed machine learning paradigm that enables collaborative training across multiple parties while ensuring data privacy. Gradient Boosting Decision Trees GBDT, such as XGBoost, have gained popularity due to their high performance and strong interpretability. Therefor...

CryptoUNets: Applying Convolutional Networks to Encrypted Data for Biomedical Image Segmentation

In this manuscript, we demonstrate the feasibility of a privacy-preserving U-Net deep learning inference framework, namely, homomorphic encryption-based U-Net inference. That is, U-Net inference can be performed solely using homomorphic encryption techniques. To our knowledge, this is the first...

Graph Privacy: a Heterogeneous Federated GNN for Trans-Border Financial Data Circulation

The sharing of external data has become a strong demand of financial institutions, but the privacy issue has led to the difficulty of interconnecting different platforms and the low degree of data openness. To effectively solve the privacy problem of financial data in trans-border flow and sharin...

Whispers of Data: Unveiling Label Distributions in Federated Learning through Virtual Client Simulation

Federated Learning enables collaborative training of a global model across multiple geographically dispersed clients without the need for data sharing. However, it is susceptible to inference attacks, particularly label inference attacks. Existing studies on label distribution inference exhibits...

An Inversion Theorem for Buffered Linear Toeplitz (BLT) Matrices and Applications to Streaming Differential Privacy

Buffered Linear Toeplitz BLT matrices are a family of parameterized lower-triangular matrices that play an important role in streaming differential privacy with correlated noise. Our main result is a BLT inversion theorem: the inverse of a BLT matrix is itself a BLT matrix with different...

WhatsApp Launches Private Processing to Enable AI Features While Protecting Message Privacy

Popular messaging app WhatsApp on Tuesday unveiled a new technology called Private Processing to enable artificial intelligence AI capabilities in a privacy-preserving manner. "Private Processing will allow users to leverage powerful optional AI features – like summarizing unread messages or...

WhatsApp Is Walking a Tightrope Between AI Features and Privacy

WhatsApp's AI tools will use a new “Private Processing” system designed to allow cloud access without letting Meta or anyone else see end-to-end encrypted chats. But experts still see risks...

What privacy? Perplexity wants your data, builds browser to track you and serve ads

AI search service Perplexity AI doesn't just want you using its app—it wants to take over your web browsing experience too. The company is planning to launch its own browser, called Comet, next month. But what does this mean for your privacy? Launched in 2022, Perplexity AI is an AI-powered searc...

What privacy? Perplexity wants your data, builds browser to track you and serve ads

AI search service Perplexity AI doesn't just want you using its app—it wants to take over your web browsing experience too. The company is planning to launch its own browser, called Comet, next month. But what does this mean for your privacy? Launched in 2022, Perplexity AI is an AI-powered searc...

SoK: Enhancing Privacy-Preserving Software Development from a Developers' Perspective

In software development, privacy preservation has become essential with the rise of privacy concerns and regulations such as GDPR and CCPA. While several tools, guidelines, methods, methodologies, and frameworks have been proposed to support developers embedding privacy into software applications...

Did:Self a Registry-Less DID Method

We introduce did:self, a Decentralized Identifier DID method that does not depend on any trusted registry for storing the corresponding DID documents. Information for authenticating a did:self subject can be disseminated using any means and without making any security assumption about the deliver...

Building Trust in Healthcare with Privacy Techniques: Blockchain in the Cloud

This study introduces a cutting-edge architecture developed for the NewbornTime project, which uses advanced AI to analyze video data at birth and during newborn resuscitation, with the aim of improving newborn care. The proposed architecture addresses the crucial issues of patient consent, data...