Privacy Challenges in Image Processing Applications

As image processing systems proliferate, privacy concerns intensify given the sensitive personal information contained in images. This paper examines privacy challenges in image processing and surveys emerging privacy-preserving techniques including differential privacy, secure multiparty...

A Framework to Prevent Biometric Data Leakage in the Immersive Technologies Domain

Doubtlessly, the immersive technologies have potential to ease people's life and uplift economy, however the obvious data privacy risks cannot be ignored. For example, a participant wears a 3D headset device which detects participant's head motion to track the pose of participant's head to match...

Safeguard-By-Development: a Privacy-Enhanced Development Paradigm for Multi-Agent Collaboration Systems

Multi-agent collaboration systems MACS, powered by large language models LLMs, solve complex problems efficiently by leveraging each agent's specialization and communication between agents. However, the inherent exchange of information between agents and their interaction with external...

“Your privacy is a promise we don’t break”: Dating app Raw exposes sensitive user data

Any app that hands over user data is a concern, but leaky dating apps are especially worrying given the sensitivity of the data involved. A relatively new app called Raw that aims to rewrite the rules of dating is the latest to trip over its coattails by exposing user data to…well, anyone who ask...

Differential Privacy for Network Assortativity

The analysis of network assortativity is of great importance for understanding the structural characteristics of and dynamics upon networks. Often, network assortativity is quantified using the assortativity coefficient that is defined based on the Pearson correlation coefficient between vertex...

Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: a Scoping Review

Explainable Artificial Intelligence XAI has emerged as a pillar of Trustworthy AI and aims to bring transparency in complex models that are opaque by nature. Despite the benefits of incorporating explanations in models, an urgent need is found in addressing the privacy concerns of providing this...

Encrypted Federated Search Using Homomorphic Encryption

The sharing of information between agencies is effective in dealing with cross-jurisdictional criminal activities; however, such sharing is often restricted due to concerns about data privacy, ownership, and compliance. Towards this end, this work has introduced a privacy-preserving federated...

Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of...

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning

The widespread adoption of Artificial Intelligence AI has been driven by significant advances in intelligent system research. However, this progress has raised concerns about data privacy, leading to a growing awareness of the need for privacy-preserving AI. In response, there has been a seismic...

A Survey on Privacy Risks and Protection in Large Language Models

Although Large Language Models LLMs have become increasingly integral to diverse applications, their capabilities raise significant privacy concerns. This survey offers a comprehensive overview of privacy risks associated with LLMs and examines current solutions to mitigate these challenges. Firs...

TikTok Slammed With €530 Million GDPR Fine for Sending E.U. Data to China

Ireland's Data Protection Commission DPC on Friday fined popular video-sharing platform TikTok €530 million $601 million for infringing data protection regulations in the region by transferring European users' data to China. "TikTok infringed the GDPR regarding its transfers of EEA European...

A False Sense of Privacy: Evaluating Textual Data Sanitization beyond Surface-Level Privacy Leakage

Whitepaper called A False Sense Of Privacy: Evaluating Textual Data Sanitization Beyond Surface-Level Privacy Leakage...

Securing the Future of IVR: AI-Driven Innovation with Agile Security, Data Regulation, and Ethical AI Integration

The rapid digitalization of communication systems has elevated Interactive Voice Response IVR technologies to become critical interfaces for customer engagement. With Artificial Intelligence AI now driving these platforms, ensuring secure, compliant, and ethically designed development practices i...

PHSafe: Disclosure Avoidance for the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC)

This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the 2020 Census Supplemental Demographic and Housing Characteristics File S-DHC. The tabulations contain statistics of counts of U.S. persons living in certain types of households, including...

Fine-Grained Manipulation Attacks to Local Differential Privacy Protocols for Data Streams

Local Differential Privacy LDP enables massive data collection and analysis while protecting end users' privacy against untrusted aggregators. It has been applied to various data types e.g., categorical, numerical, and graph data and application settings e.g., static and streaming. Recent finding...

Capability-Based Multi-Tenant Access Management in Crowdsourced Drone Services

We propose a capability-based access control method that leverages OAuth 2.0 and Verifiable Credentials VCs to share resources in crowdsourced drone services. VCs securely encode claims about entities, offering flexibility. However, standardized protocols for VCs are lacking, limiting their...

SafeTab-P: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File a (Detailed DHC-A)

This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the Detailed Demographic and Housing Characteristics File A Detailed DHC-A of the 2020 Census. The tabulations contain statistics counts of demographic characteristics of the entire population of...

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...