Λ\Lambda-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI

In the wake of the burgeoning expansion of generative artificial intelligence (AI) services, the computational demands inherent to these technologies frequently necessitate cloud-powered computational offloading, particularly for resource-constrained mobile devices. These services commonly employ prompts to steer the generative process, and both the prompts and the resultant content, such as text and images, may harbor privacy-sensitive or confidential information, thereby elevating security and privacy risks. To mitigate these concerns, we introduce $\Lambda$-Split, a split computing framework to facilitate computational offloading while simultaneously fortifying data privacy against risks such as eavesdropping and unauthorized access. In $\Lambda$-Split, a generative model, usually a deep neural network (DNN), is partitioned into three sub-models and distributed across the user's local device and a cloud server: the input-side and output-side sub-models are allocated to the local, while the intermediate, computationally-intensive sub-model resides on the cloud server. This architecture ensures that only the hidden layer outputs are transmitted, thereby preventing the external transmission of privacy-sensitive raw input and output data. Given the black-box nature of DNNs, estimating the original input or output from intercepted hidden layer outputs poses a significant challenge for malicious eavesdroppers. Moreover, $\Lambda$-Split is orthogonal to traditional encryption-based security mechanisms, offering enhanced security when deployed in conjunction. We empirically validate the efficacy of the $\Lambda$-Split framework using Llama 2 and Stable Diffusion XL, representative large language and diffusion models developed by Meta and Stability AI, respectively. Our $\Lambda$-Split implementation is publicly accessible at https://github.com/nishio-laboratory/lambda_split.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications

This study demonstrates the feasibility of point cloud-based proactive link quality prediction for millimeter-wave (mmWave) communications. Previous studies have proposed machine learning-based methods to predict received signal strength for future time periods using time series of depth images to mitigate the line-of-sight (LOS) path blockage by pedestrians in mmWave communication. However, these image-based methods have limited applicability due to privacy concerns as camera images may contain sensitive information. This study proposes a point cloud-based method for mmWave link quality prediction and demonstrates its feasibility through experiments. Point clouds represent three-dimensional (3D) spaces as a set of points and are sparser and less likely to contain sensitive information than camera images. Additionally, point clouds provide 3D position and motion information, which is necessary for understanding the radio propagation environment involving pedestrians. This study designs the mmWave link quality prediction method and conducts realistic indoor experiments, where the link quality fluctuates significantly due to human blockage, using commercially available IEEE 802.11ad-based 60 GHz wireless LAN devices and Kinect v2 RGB-D camera and Velodyne VLP-16 light detection and ranging (LiDAR) for point cloud acquisition. The experimental results showed that our proposed method can predict future large attenuation of mmWave received signal strength and throughput induced by the LOS path blockage by pedestrians with comparable or superior accuracy to image-based prediction methods. Hence, our point cloud-based method can serve as a viable alternative to image-based methods.Comment: Submitted to IEEE Transactions on Machine Learning in Communications and Networkin

Trans-Inpainter: Wireless Channel Information- Guided Image Restoration via Multimodal Transformer

Image inpainting is a critical computer vision task to restore missing or damaged image regions. In this paper, we propose Trans-Inpainter, a novel multimodal image inpainting method guided by Channel State Information (CSI) data. Leveraging the power of transformer architectures, Trans-Inpainter effectively extracts visual information from CSI time sequences, enabling high-quality and realistic image inpainting. To evaluate its performance, we compare Trans-Inpainter with RF-Inpainter, the state-of-the-art radio frequency (RF) signal-based image inpainting technique. Through comprehensive experiments, Trans-Inpainter consistently demonstrates superior performance in various scenarios. Additionally, we investigate the impact of CSI data variations on Trans-Inpainter's imaging ability, analyzing individual sensor data, fused data from multiple sensors, and altered CSI matrix dimensions. These insights provide valuable references for future wireless sensing and computer vision studies

Leukotriene receptor antagonist attenuated airway inflammation and hyperresponsiveness in a double-stranded RNA-induced asthma exacerbation model

Background: Viral infections are the most common triggers of asthma exacerbation, but the key molecules involved in this process have not been fully identified. Although cysteinyl leukotrienes (cysLTs) have been postulated as the key mediators, their precise roles remain largely unclear. To investigate the roles of cysLTs in virus-induced asthma exacerbation, we developed a murine model using a viral double-stranded RNA analog, polyinosinic–polycytidylic acid (poly I:C), and analyzed the effect of leukotriene receptor antagonist (LTRA) administration. Methods: A/J mice were immunized with ovalbumin (OVA) + alum (days 0, 28, 42, and 49), followed by intranasal challenge with OVA (phase 1: days 50–52) and poly I:C (phase 2: days 53–55). Montelukast was administered during poly I:C challenge (phase 2) in the reliever model or throughout the OVA and poly I:C challenges (phases 1 and 2) in the controller model. Airway responsiveness to acetylcholine chloride was assessed, and bronchoalveolar lavage (BAL) was performed on day 56. Results: Administration of poly I:C to OVA-sensitized and -challenged mice increased the number of eosinophils and levels of IL-13, IL-9, CCL3, and CXCL1 in BAL fluid (BALF) and tended to increase airway responsiveness. Montelukast significantly attenuated the poly I:C-induced increase in the number of eosinophils and levels of IL-13, IL-9, and CCL3 in BALF and airway hyperresponsiveness in both the reliever and controller models. Conclusions: This is the first report showing that LTRA functionally suppressed the pathophysiology of a virus-induced asthma exacerbation model, suggesting the importance of cysLTs as a potential treatment target