Browse All

Mobile crowdsensing (MCS, a system where mobile users contribute data to solve problems) needs ways to verify that the collected data is truthful while protecting people's privacy. This paper proposes two new truth discovery schemes: RsAnonTD for systems with a stable group of users, and McFeKDeTD for systems where workers join and leave dynamically. Both schemes use cryptographic techniques (ring signature, perturbation, and functional encryption with zero-knowledge proofs, which are mathematical ways to verify information without revealing details) to keep data private and defend against attacks like data forgery and tampering, while also reducing computational overhead by up to 98% compared to existing approaches.

Diffusion models (AI systems that generate images from text descriptions) can be misused to create unauthorized portraits or copies of artistic styles through personalization, which threatens privacy and copyright. PersGuard is a new defense framework that embeds protective backdoors (hidden mechanisms) into these models before release, so that if someone tries to personalize the model with protected images, it generates predetermined protective outputs instead, while still working normally for unprotected images.

SilentNoise addresses a problem in differential privacy (DP, a method for analyzing data while protecting individual privacy), which traditionally relies on one trusted party holding all sensitive data, creating a security risk. The researchers propose a decentralized system using secure multiparty computation (MPC, where multiple parties jointly compute results without fully revealing their individual data) that allows noise (random data added for privacy) to be generated securely even when some parties act maliciously, improving both efficiency and accuracy compared to previous approaches.

Garland is a system for recommendation engines that use graph neural networks (GNNs, which are AI models that learn patterns from interconnected user-item relationships) in federated settings, where data stays on users' devices instead of being sent to one central server. The system addresses a key problem: untrusted servers that help expand users' local data can spy on both item information and user relationships, so Garland uses secret-shared shuffle (a cryptographic technique that mixes data while keeping it encrypted) to protect privacy while still catching if a malicious server tries to cheat.

This newsletter covers several AI industry developments, including Elon Musk losing his lawsuit against OpenAI (a company creating large language models, which are AI systems trained on large amounts of text data) because he sued too late under statutes of limitations rather than on the merits of whether OpenAI violated its nonprofit mission. Other stories include Anduril and Meta developing augmented-reality smart glasses (wearable devices that overlay digital information on the physical world) for military use with eye-tracking controls, and Google preparing to showcase its AI capabilities at its I/O developer conference while facing competition from other AI companies.

OpenAI is improving how people can verify where AI-generated images and audio come from by using multiple approaches: adding C2PA conformance (a cross-industry standard using metadata and cryptographic signatures to attach source information to content), partnering with Google to embed invisible watermarks called SynthID into images, and releasing a public tool to verify if images came from OpenAI. These layered approaches help protect provenance information (details about content's origin and creation) even when it's edited, downloaded, or shared across different platforms.

Anthropic, an AI company, is suing the U.S. Department of Defense in federal court after the DOD labeled it a "supply chain risk" (a designation suggesting it threatens national security), which requires defense contractors to stop using Anthropic's Claude AI models in military work. The court judges questioned whether the DOD properly justified this blacklisting, with one judge calling it a "spectacular overreach," while the DOD argued it needed to act quickly to notify agencies about the risk.