TikTok Is Now Collecting Even More Data About Its Users. According to its new privacy policy, TikTok now collects more data on its users, including their precise location, after majority ownership officially switched to a group based in the US. When TikTok users in the US opened the app today, they were greeted with a pop-up asking them to agree to the social media platform's new terms of service and privacy policy before they could resume scrolling. These changes are part of TikTok's transition to new ownership. In order to continue operating in the US, TikTok was compelled by the US government to transition from Chinese control to a new, American-majority corporate entity.

Federal records show CBP is moving from testing small drones to making them standard surveillance tools, expanding a network that can follow activity in real time and extend well beyond the border. US Customs and Border Protection is quietly doubling down on a surveillance strategy built around human-portable drones, according to federal contracting records reviewed by WIRED. The shift is pushing border enforcement toward a distributed system that can track activity in real time and, critics warn, may extend well beyond the border. New market research conducted this month shows that, rather than relying on larger, centralized drone platforms, CBP is concentrating on lightweight uncrewed aircraft that can be launched quickly by small teams, remain operational under environmental stress, and relay surveillance data directly to frontline units. The documents emphasize portability, fast setup, and integration with equipment already used by border patrol.

The Bluetooth tracker maker is adding free and paid SOS features to its products, including emergency contact alerts, silent alarms, and real-time location sharing. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Think of Bluetooth trackers and safety in the past few years and your first thought might be the misuse of Apple AirTags and similar devices against women in domestic abuse and stalking cases. Alongside collaborative initiatives to counter and shut down these malicious uses (such as the IETF's Detection of Unwanted Location Trackers, or DULT, standard), tracker makers themselves are flipping the script, turning tech that has been used to monitor women against their will into tech that protects them.

In modern times, people have numerous online accounts, but they rarely read the Terms of Service or Privacy Policy of those sites despite claiming otherwise. This paper introduces PoliAnalyzer, a neuro-symbolic system that assists users with personalized privacy policy analysis. PoliAnalyzer uses Natural Language Processing (NLP) to extract formal representations of data usage practices from policy texts. In favor of deterministic, logical inference is applied to compare user preferences with the formal privacy policy representation and produce a compliance report. To achieve this, we extend an existing formal Data Terms of Use policy language to model privacy policies as app policies and user preferences as data policies. In our evaluation using our enriched PolicyIE dataset curated by legal experts, PoliAnalyzer demonstrated high accuracy in identifying relevant data usage practices, achieving F1-score of 90-100% across most tasks. Additionally, we demonstrate how PoliAnalyzer can model diverse user data-sharing preferences, derived from prior research as 23 user profiles, and perform compliance analysis against the top 100 most-visited websites. This analysis revealed that, on average, 95.2% of a privacy policy's segments do not conflict with the analyzed user preferences, enabling users to concentrate on understanding the 4.8% (636 / 13205) that violates preferences, significantly reducing cognitive burden. Further, we identified common practices in privacy policies that violate user expectations - such as the sharing of location data with 3rd parties. This paper demonstrates that PoliAnalyzer can support automated personalized privacy policy analysis at scale using off-the-shelf NLP tools. This sheds light on a pathway to help individuals regain control over their data and encourage societal discussions on platform data practices to promote a fairer power dynamic.

Cell-free Integrated Sensing and Communication (ISAC) aims to revolutionize 6th Generation (6G) networks. By combining distributed access points with ISAC capabilities, it boosts spectral efficiency, situational awareness, and communication reliability. Channel estimation is a critical step in cell-free ISAC systems to ensure reliable communication, but its performance is usually limited by challenges such as pilot contamination and noisy channel estimates. This paper presents a novel framework leveraging sensing information as a key input within a Conditional Denoising Diffusion Model (CDDM). In this framework, we integrate CDDM with a Multimodal Transformer (MMT) to enhance channel estimation in ISAC-enabled cell-free systems. The MMT encoder effectively captures inter-modal relationships between sensing and location data, enabling the CDDM to iteratively denoise and refine channel estimates. Simulation results demonstrate that the proposed approach achieves significant performance gains. As compared with Least Squares (LS) and Minimum Mean Squared Error (MMSE) estimators, the proposed model achieves normalized mean squared error (NMSE) improvements of 8 dB and 9 dB, respectively. Moreover, we achieve a 27.8% NMSE improvement compared to the traditional denoising diffusion model (TDDM), which does not incorporate sensing channel information. Additionally, the model exhibits higher robustness against pilot contamination and maintains high accuracy under challenging conditions, such as low signal-to-noise ratios (SNRs). According to the simulation results, the model performs well for users near sensing targets by leveraging the correlation between sensing and communication channels.

A huge data breach has leaked over 50,000 profiles from the'Gay Daddy' dating app, cybersecurity researchers have discovered. The exposed data contains extremely sensitive information including users' names, ages, location data and HIV status. According to experts from Cybernews, the exposed database also contains over 124,000 private messages and photos – many of which are explicit. While the app markets itself as a'private and anonymous community', researchers say the information could be accessed by anyone with'basic technical knowledge'. Researchers say the app's'devastating' security failure puts its users at serious risk of blackmail, exploitation and even physical harm.

Sixth generation (6G) wireless technology is anticipated to introduce Integrated Sensing and Communication (ISAC) as a transformative paradigm. ISAC unifies wireless communication and RADAR or other forms of sensing to optimize spectral and hardware resources. This paper presents a pioneering framework that leverages ISAC sensing data to enhance beam selection processes in complex indoor environments. By integrating multi-modal transformer models with a multi-agent contextual bandit algorithm, our approach utilizes ISAC sensing data to improve communication performance and achieves high spectral efficiency (SE). Specifically, the multi-modal transformer can capture inter-modal relationships, enhancing model generalization across diverse scenarios. Experimental evaluations on the DeepSense 6G dataset demonstrate that our model outperforms traditional deep reinforcement learning (DRL) methods, achieving superior beam prediction accuracy and adaptability. In the single-user scenario, we achieve an average SE regret improvement of 49.6% as compared to DRL. Furthermore, we employ transfer reinforcement learning to reduce training time and improve model performance in multi-user environments. In the multi-user scenario, this approach enhances the average SE regret, which is a measure to demonstrate how far the learned policy is from the optimal SE policy, by 19.7% compared to training from scratch, even when the latter is trained 100 times longer.

Currently, depression treatment relies on closely monitoring patients response to treatment and adjusting the treatment as needed. Using self-reported or physician-administrated questionnaires to monitor treatment response is, however, burdensome, costly and suffers from recall bias. In this paper, we explore using location sensory data collected passively on smartphones to predict treatment outcome. To address heterogeneous data collection on Android and iOS phones, the two predominant smartphone platforms, we explore using domain adaptation techniques to map their data to a common feature space, and then use the data jointly to train machine learning models. Our results show that this domain adaptation approach can lead to significantly better prediction than that with no domain adaptation. In addition, our results show that using location features and baseline self-reported questionnaire score can lead to F1 score up to 0.67, comparable to that obtained using periodic self-reported questionnaires, indicating that using location data is a promising direction for predicting depression treatment outcome.

Some of the world's most popular apps are likely being co-opted by rogue members of the advertising industry to harvest sensitive location data on a massive scale, with that data ending up with a location data company whose subsidiary has previously sold global location data to US law enforcement. The thousands of apps, included in hacked files from location data company Gravy Analytics, include everything from games like Candy Crush and dating apps like Tinder to pregnancy tracking and religious prayer apps across both Android and iOS. Because much of the collection is occurring through the advertising ecosystem--not code developed by the app creators themselves--this data collection is likely happening without users' or even app developers' knowledge. This article was created in partnership with 404 Media, a journalist-owned publication covering how technology impacts humans. "For the first time publicly, we seem to have proof that one of the largest data brokers selling to both commercial and government clients appears to be acquiring their data from the online advertising'bid stream,'" rather than code embedded into the apps themselves, Zach Edwards, senior threat analyst at cybersecurity firm Silent Push and who has followed the location data industry closely, tells 404 Media after reviewing some of the data.

It may be a new year, but the hacks, scams, and dangerous people lurking online haven't gone anywhere. Just a day before the ball dropped, the United States Treasury Department said it had been hacked. Officials believe the attackers are an as-yet-unidentified Advanced Persistent Threat group linked to China's government that exploited flaws in remote tech support software made by BeyondTrust to carry out what the Treasury Department described as a "major" breach. The company told the Treasury on December 8 that the attackers stole an authentication key, which ultimately allowed them to access department computers. While the Treasury says the attackers were only able to steal "certain unclassified documents," new details have already begun to emerge, which we'll get into more below.