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When Vision Misleads, Let Location Speak: A Worldwide Image Geo-Localization Method via Location Attention Mechanism and Large Multimodal Models
arXiv:2606.08918v1 Announce Type: new Abstract: Worldwide image geo-localization aims to determine the capture location of an image on a global scale. Existing methods often mislocalize images by matching them to visually similar scenes from different geographic regions, which limits reliability in practical applications. To address this issue, we propose TransGeoCLIP, a novel retrieval-based framework that integrates a location attention mechanism and large multimodal models (LMMs).
Protecting K-Nearest Neighbor Queries from Location Inference Attacks
Announce Type: new Abstract: The k-nearest neighbor query (kNNQ) is a core component of modern location-based services (LBS) and has been widely adopted in popular features such as ``people nearby''. However, its potential privacy risks have long been overlooked. In this work, we present the first two attacks against kNNQ, namely the geometric intersection location inference attack (GI-LIA) and the zero-order optimization location inference attack (ZO-LIA), revealing the inherent location...
Channel Chart Location Privacy Based on Geo-Indistinguishability
Announce Type: new Abstract: Channel charting enables location-based services (LBSs) without requiring explicit position information by using pseudo-locations from the channel chart. While this property implies inherent privacy advantages, it does not provide formal privacy guarantees. In this work, we address location privacy in channel charting referred to as chart location indistinguishability (CLI), which extends geo-indistinguishability (GI) to channel charting representations.
Chrome on Android will now let you share your approximate location
Chrome on Android will now let you share your approximate location In a minor privacy win for Android users, Google announced today that Chrome's Android app will now have the ability to share your approximate location with websites, instead of giving them access to your precise location. The feature gives you a bit more flexibility, without giving up your privacy to random websites. For example, if you're just looking for information about the weather or something else in your general area,...
SQEEZ: Energy-efficient Location Sharing for Mobile Ad Hoc Networks
arXiv:2605.31339v1 Announce Type: new Abstract: Periodic network-wide dissemination of node location data is crucial for shared situational awareness and collaborative mapping in mobile ad hoc and mesh networks for public safety, disaster relief, and military. A key challenge is to provide maximally accurate location information with minimal energy expenditure on part of the nodes. We present SQEEZ: a mechanism for reducing the Position Location Information (PLI) load that combines two...
Strategyproof Mechanisms for Euclidean Facility Location Problems under $L_p$-norm Social Cost
arXiv:2606.08621v1 Announce Type: new Abstract: We study strategyproof mechanisms for eliciting agents' location preferences truthfully in the Euclidean plane $\mathbb R^2$ and locating a facility so as to minimize the $L_p$-norm social cost, defined as the $L_p$-norm of the vector of distances from the facility to the agents' preferred locations, for any $p \ge 1$. While the cases $p=1$ and $p=\infty$ have been well-studied, open questions remain about the optimal approximation ratios...
Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
Announce Type: new Abstract: Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While recent methods incorporating large language models have shown strong reasoning capabilities and promising results, they typically formulate the prediction task as a one-step trajectory-to-location mapping problem, making predictions prone to shallow trajectory correlations and historical frequency bias. We argue that...
RadioDiff-Inv2: Differentiable Diffusion Inversion under Location Drift from Sparse Noisy Measurements for Radio Map Estimation
arXiv:2606.08439v1 Announce Type: new Abstract: Radio map (RM) estimation is a key enabler for environment-aware optimization in 6G wireless networks. In practice, RM construction increasingly relies on crowdsourced received signal strength (RSS) feedback that is inherently sparse and noisy. A further and often overlooked challenge is location drift, whereby privacy constraints and user mobility cause reported sampling coordinates to deviate from the true measurement locations.
GPU Fingerprinting for Location Verification
arXiv:2605.01930v2 Announce Type: replace Abstract: Robust governance of GPU chips is important for mitigating risks from unauthorized development of advanced AI models. Current methods for monitoring chip location rely on ping-based protocols backed by cryptographic keys stored on-chip. However, these keys can potentially be extracted by adversaries with physical access, compromising the location verification protocol.
Take Action: LAPD Removed Crime Location Data. Here's Why It Matters
Take Action: LAPD Removed Crime Location Data. Here's Why It Matters. Dear SpotCrime Subscriber, For years, residents across Los Angeles relied on public crime data to stay informed about safety in their neighborhoods.