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...

Theoretically Unmasking Inference Attacks against LDP-Protected Clients in Federated Vision Models

Federated Learning enables collaborative learning among clients via a coordinating server while avoiding direct data sharing, offering a perceived solution to preserve privacy. However, recent studies on Membership Inference Attacks MIAs have challenged this notion, showing high success rates...

Big Bird: Privacy Budget Management for W3C'S Privacy-Preserving Attribution API

Privacy-preserving advertising APIs like Privacy-Preserving Attribution PPA are designed to enhance web privacy while enabling effective ad measurement. PPA offers an alternative to cross-site tracking with encrypted reports governed by differential privacy DP, but current designs lack a principl...

Differentially Private Distribution Release of Gaussian Mixture Models Via KL-Divergence Minimization

Gaussian Mixture Models GMMs are widely used statistical models for representing multi-modal data distributions, with numerous applications in data mining, pattern recognition, data simulation, and machine learning. However, recent research has shown that releasing GMM parameters poses significan...

Optimal Allocation of Privacy Budget on Hierarchical Data Release

Releasing useful information from datasets with hierarchical structures while preserving individual privacy presents a significant challenge. Standard privacy-preserving mechanisms, and in particular Differential Privacy, often require careful allocation of a finite privacy budget across differen...

Privid: A Privacy-Preserving Surveillance Video Analytics System

A group of academics has designed a new system known as "Privid" that enables video analytics in a privacy-preserving manner to combat concerns with invasive tracking. "We're at a stage right now where cameras are practically ubiquitous. If there's a camera on every street corner, every place you...