PDLRecover: Privacy-preserving Decentralized Model Recovery with Machine Unlearning

Decentralized learning is vulnerable to poison attacks, where malicious clients manipulate local updates to degrade global model performance. Existing defenses mainly detect and filter malicious models, aiming to prevent a limited number of attackers from corrupting the global model. However,...

Foundation of Affective Computing and Interaction

This book provides a comprehensive exploration of affective computing and human-computer interaction technologies. It begins with the historical development and basic concepts of human-computer interaction, delving into the technical frameworks and practical applications of emotional computing,...

Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers

We study privacy leakage in the reasoning traces of large reasoning models used as personal agents. Unlike final outputs, reasoning traces are often assumed to be internal and safe. We challenge this assumption by showing that reasoning traces frequently contain sensitive user data, which can be...

SoK: Advances and Open Problems in Web Tracking

Web tracking is a pervasive and opaque practice that enables personalized advertising, retargeting, and conversion tracking. Over time, it has evolved into a sophisticated and invasive ecosystem, employing increasingly complex techniques to monitor and profile users across the web. The research...

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning

Despite federated learning FL's potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning CFL has emerged to address this challenge by partitioning users into clusters according to their...

Private Continual Counting of Unbounded Streams

We study the problem of differentially private continual counting in the unbounded setting where the input size $n$ is not known in advance. Current state-of-the-art algorithms based on optimal instantiations of the matrix mechanism cannot be directly applied here because their privacy guarantees...

Dual Protection Ring: User Profiling Via Differential Privacy and Service Dissemination through Private Information Retrieval

User profiling is crucial in providing personalised services, as it relies on analyzing user behaviour and preferences to deliver targeted services. This approach enhances user experience and promotes heightened engagement. Nevertheless, user profiling also gives rise to noteworthy privacy...

Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference

Differential privacy DP auditing aims to provide empirical lower bounds on the privacy guarantees of DP mechanisms like DP-SGD. While some existing techniques require many training runs that are prohibitively costly, recent work introduces one-run auditing approaches that effectively audit DP-SGD...

Anonymous Authentication using Attribute-based Encryption

In today's digital age, personal data is constantly at risk of compromise. Attribute-Based Encryption ABE has emerged as a promising approach to privacy-preserving data protection. This paper proposes an anonymous authentication mechanism based on ABE, which allows users to authenticate without...

Buy It Now, Track Me Later: Attacking User Privacy Via Wi-Fi AP Online Auctions

Static and hard-coded layer-two network identifiers are well known to present security vulnerabilities and endanger user privacy. In this work, we introduce a new privacy attack against Wi-Fi access points listed on secondhand marketplaces. Specifically, we demonstrate the ability to remotely...

A Locally Differential Private Coding-Assisted Succinct Histogram Protocol

A succinct histogram captures frequent items and their frequencies across clients and has become increasingly important for large-scale, privacy-sensitive machine learning applications. To develop a rigorous framework to guarantee privacy for the succinct histogram problem, local differential...

Zero-Knowledge Proof-Of-Location Protocols for Vehicle Subsidies and Taxation Compliance

This paper introduces a new set of privacy-preserving mechanisms for verifying compliance with location-based policies for vehicle taxation, or for electric vehicle EV subsidies, using Zero-Knowledge Proofs ZKPs. We present the design and evaluation of a Zero-Knowledge Proof-of-Location ZK-PoL...

Tracker Installations Are Not Created Equal: Understanding Tracker Configuration of Form Data Collection

Targeted advertising is fueled by the comprehensive tracking of users' online activity. As a result, advertising companies, such as Google and Meta, encourage website administrators to not only install tracking scripts on their websites but configure them to automatically collect users' Personall...

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset

We investigate the contents of web-scraped data for training AI systems, at sizes where human dataset curators and compilers no longer manually annotate every sample. Building off of prior privacy concerns in machine learning models, we ask: What are the legal privacy implications of web-scraped...

A Novel Approach to Differential Privacy with Alpha Divergence

As data-driven technologies advance swiftly, maintaining strong privacy measures becomes progressively difficult. Conventional $ε, δ$-differential privacy, while prevalent, exhibits limited adaptability for many applications. To mitigate these constraints, we present alpha differential privacy AD...

Postbox 安全漏洞

Postbox is an email client software from Postbox, Inc. A security vulnerability exists in Postbox that stems from allowing dynamic library injection, which could lead to a local attacker bypassing TCC...

Mattel’s going to make AI-powered toys, kids’ rights advocates are worried

Toy company Mattel has announced a deal with OpenAI to create AI-powered toys, but digital rights advocates have urged caution. In a press release last week, the owner of the Barbie brand signed a "strategic collaboration" with the AI company, which owns ChatGPT. "By using OpenAI's technology,...

Self-Driving Car Video Footage

Two articles crossed my path recently. First, a discussion of all the video Waymo has from outside its cars: in this case related to the LA protests. Second, a discussion of all the video Tesla has from inside its cars. Lots of things are collecting lots of video of lots of other things. How and...

Omise: PII Exposure via Email Confirmation Link – Email Embedded in Token & Leaked via Wayback Machine

The vulnerability involved the exposure of personally identifiable information PII, specifically email addresses, through an email confirmation link used by Omise. The email address was embedded directly in a token that was visible in the URL. This token was subsequently archived by the Wayback...

Black-Box Privacy Attacks on Shared Representations in Multitask Learning

Multitask learning MTL has emerged as a powerful paradigm that leverages similarities among multiple learning tasks, each with insufficient samples to train a standalone model, to solve them simultaneously while minimizing data sharing across users and organizations. MTL typically accomplishes th...