PRE+: dual of proxy re-encryption for secure cloud data sharing service

With the rapid development of very large, diverse, complex, and distributed datasets generated from internet transactions, emails, videos, business information systems, manufacturing industry, sensors and internet of things etc., cloud and big data computation have emerged as a cornerstone of modern applications. Indeed, on the one hand, cloud and big data applications are becoming a main driver for economic growth. On the other hand, cloud and big data techniques may threaten people and enterprises’ privacy and security due to ever increasing exposure of their data to massive access. In this paper, aiming at providing secure cloud data sharing services in cloud storage, we propose a scalable and controllable cloud data sharing framework for cloud users (called: Scanf). To this end, we introduce a new cryptographic primitive, namely, PRE+, which can be seen as the dual of traditional proxy re-encryption (PRE) primitive. All the traditional PRE schemes until now require the delegator (or the delegator and the delegatee cooperatively) to generate the re-encryption keys. We observe that this is not the only way to generate the re-encryption keys, the encrypter also has the ability to generate re-encryption keys. Based on this observation, we construct a new PRE+ scheme, which is almost the same as the traditional PRE scheme except the re-encryption keys generated by the encrypter. Compared with PRE, our PRE+ scheme can easily achieve the non-transferable property and message-level based fine-grained delegation. Thus our Scanf framework based on PRE+ can also achieve these two properties, which is very important for users of cloud storage sharing service. We also roughly evaluate our PRE+ scheme’s performance and the results show that our scheme is efficient and practica for cloud data storage applications.Peer ReviewedPostprint (author's final draft

Huber Kalman Filter for Wi-Fi based Vehicle Driver\u27s Respiration Detection

The use of breath detection in vehicles can reduce the number of vehicular accidents caused by drivers in poor physical condition. Prior studies of contactless respiration detection mainly targeted a static person. However, there are emerging applications to sense a driver, with emphasis on contactless methods. For example, being able to detect a driver\u27s respiration while driving by using a vehicular Wi-Fi system can significantly enhance driving safety. The sensing system can be mounted on the back of the driver\u27s seat, and it can sense the tiny chest displacement of the driver via Wi-Fi signals. The body displacement and car vibrations could introduce significant noise in the sensed signal. The noise then needs to be filtered to obtain the driver\u27s respiration. In this work, the noise in the sensed signal is proposed to be reduced using a Huber Kalman filter to restore the original respiration curve. Through several experiments in terms of different drivers, different car models, multiple passengers, and abnormal breathing, we demonstrate the accuracy and robustness of the Huber Kalman filter in driver\u27s respiration

Chronic oleoylethanolamide treatment attenuates diabetes-induced mice encephalopathy by triggering peroxisome proliferator-activated receptor alpha in the hippocampus.

Brain is a site of diabetic end-organ damage. Diabetes-associated cognitive dysfunction, referred as "diabetic encephalopathy" (DE) has been coined for the patients with type 2 diabetes mellitus showing decline in their cognitive function, especially weak episodic memory, cognitive inflexibility and poor psychomotor performance leading towards Alzheimer’s disease. Current evidence supported that aberrant synapses, energy metabolism imbalance, advanced glycation end products (AGEs) accumulation and Tau hyperphosphorylation are associated with cognition deficits induced by diabetes. Oleoylethanolamide (OEA), an endogenous peroxisome proliferator-activated receptor alpha (PPARα) agonist, has anti-hyperlipidemia, anti-inflammatory and neuroprotective activities. However, the effect of OEA on DE is unknown. Therefore, we tested its influence against cognitive dysfunction in high fat diet and streptozotocin (HFD + STZ)-induced diabetic C57BL/6J and PPARα--/- mice using Morris water maze (MWM) test. Neuron staining, dementia markers and neuroplasticity in the hippocampus were assessed to evaluate the neuropathological changes. The results showed that chronic OEA treatment significantly lowered hyperglycemia, recovered cognitive performance, reduced dementia markers, and inhibited hippocampal neuron loss and neuroplasticity impairments in diabetic mice. In contrast, the changes in MWM performance and neuron loss were not observed in PPARα knockout mice via OEA administration. These results indicated that OEA may provide a potential alternative therapeutic for DE by activating PPARα signaling

Theranostic Quercetin Nanoparticle for Treatment of Hepatic Fibrosis.

The progression of hepatic fibrosis can lead to cirrhosis and hepatic failure, but the development of antifibrotic drugs have faced the challenges of poor effectiveness and targeted specificity. Herein, a theranostic strategy was carried to encapsulate a natural medicine (Quercetin, QR) into hepatitis B core (HBc) protein nanocages (NCs) for imaging and targeted treatment of hepatic fibrosis. It was noted that nanoparticles (RGD-HBc/QR) with surface-displayed RGD targeting ligand exhibit a rather high selectivity toward activated HSCs via the binding affinity with integrin αvβ3, and an efficient inhibition of proliferation and activation of hepatic stellate cells (HSCs) in vitro and in vivo. Once encapsulated in quercetin-gadolinium complex and/or labeled with the NIR fluorescent probes (Cy5.5), the resulting nanoparticles (RGD-HBc/QGd) show great potential as NIR fluorescent and magnetic resonance imaging contrast agents for hepatic fibrosis in vivo. Therefore, the multifunctional integrin-targeted nanoparticles could selectively deliver QR to the activated HSCs, and may provide an effective antifibrotic theranostic strategy

Type I Interferons and Interferon Regulatory Factors Regulate TNF-Related Apoptosis-Inducing Ligand (TRAIL) in HIV-1-Infected Macrophages

TNF-related apoptosis-inducing ligand (TRAIL) is a member of the TNF family that participates in HIV-1 pathogenesis through the depletion of CD4+ T cells. TRAIL is expressed on the cell membrane of peripheral immune cells and can be cleaved into a soluble, secreted form. The regulation of TRAIL in macrophages during HIV-1 infection is not completely understood. In this study, we investigated the mechanism(s) of TRAIL expression in HIV-1-infected macrophages, an important cell type in HIV-1 pathogenesis. A human monocyte-derived macrophage (MDM) culture system was infected with macrophage-tropic HIV-1ADA, HIV-1JR-FL, or HIV-1BAL strains. TRAIL, predominantly the membrane-bound form, increased following HIV-1 infection. We found that HIV-1 infection also induced interferon regulatory factor (IRF)-1, IRF-7 gene expression and signal transducers and activators of transcription 1 (STAT1) activation. Small interfering RNA knockdown of IRF-1 or IRF-7, but not IRF-3, reduced STAT1 activation and TRAIL expression. Furthermore, the upregulation of IRF-1, IRF-7, TRAIL, and the activation of STAT1 by HIV-1 infection was reduced by the treatment of type I interferon (IFN)-neutralizing antibodies. In addition, inhibition of STAT1 by fludarabine abolished IRF-1, IRF-7, and TRAIL upregulation. We conclude that IRF-1, IRF-7, type I IFNs, and STAT1 form a signaling feedback loop that is critical in regulating TRAIL expression in HIV-1-infected macrophages

PRE+: dual of proxy re-encryption for secure cloud data sharing service

With the rapid development of very large, diverse, complex, and distributed datasets generated from internet transactions, emails, videos, business information systems, manufacturing industry, sensors and internet of things etc., cloud and big data computation have emerged as a cornerstone of modern applications. Indeed, on the one hand, cloud and big data applications are becoming a main driver for economic growth. On the other hand, cloud and big data techniques may threaten people and enterprises’ privacy and security due to ever increasing exposure of their data to massive access. In this paper, aiming at providing secure cloud data sharing services in cloud storage, we propose a scalable and controllable cloud data sharing framework for cloud users (called: Scanf). To this end, we introduce a new cryptographic primitive, namely, PRE+, which can be seen as the dual of traditional proxy re-encryption (PRE) primitive. All the traditional PRE schemes until now require the delegator (or the delegator and the delegatee cooperatively) to generate the re-encryption keys. We observe that this is not the only way to generate the re-encryption keys, the encrypter also has the ability to generate re-encryption keys. Based on this observation, we construct a new PRE+ scheme, which is almost the same as the traditional PRE scheme except the re-encryption keys generated by the encrypter. Compared with PRE, our PRE+ scheme can easily achieve the non-transferable property and message-level based fine-grained delegation. Thus our Scanf framework based on PRE+ can also achieve these two properties, which is very important for users of cloud storage sharing service. We also roughly evaluate our PRE+ scheme’s performance and the results show that our scheme is efficient and practica for cloud data storage applications.Peer Reviewe

Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter

The reliability of battery fault diagnosis depends on an accurate estimation of the state of charge and battery characterizing parameters. This paper presents a fault diagnosis method based on an adaptive unscented Kalman filter to diagnose the parameter bias faults for a Li-ion battery in real time. The first-order equivalent circuit model and relationship between the open circuit voltage and state of charge are established to describe the characteristics of the Li-ion battery. The parameters in the equivalent circuit model are treated as system state variables to set up a joint state and parameter space equation. The algorithm for fault diagnosis is designed according to the estimated parameters. Two types of fault of the Li-ion battery, including internal ohmic resistance fault and diffusion resistance faults, are studied as a case to validate the effectiveness of the algorithm. The experimental results show that the proposed approach in this paper has effective tracking ability, better estimation accuracy, and reliable diagnosis for Li-ion batteries