A New Enforcement on Declassification with Reachability Analysis

Language-based information flow security aims to decide whether an action-observable program can unintentionally leak confidential information if it has the authority to access confidential data. Recent concerns about declassification polices have provided many choices for practical intended information release, but more precise enforcement mechanism for these policies is insufficiently studied. In this paper, we propose a security property on the where-dimension of declassification and present an enforcement based on automated verification. The approach automatically transforms the abstract model with a variant of self-composition, and checks the reachability of illegal-flow state of the model after transformation. The self-composition is equipped with a store-match pattern to reduce the state space and to model the equivalence of declassified expressions in the premise of property. The evaluation shows that our approach is more precise than type-based enforcement.Comment: 7 pages, this is a full version of the work presented on 2011 IEEE INFOCOM Workshop

A Sandwich Electrochemical Immunosensor Using Magnetic DNA Nanoprobes for Carcinoembryonic Antigen

A novel magnetic nanoparticle-based electrochemical immunoassay of carcinoembryonic antigen (CEA) was designed as a model using CEA antibody-functionalized magnetic beads [DNA/Fe3O4/ZrO2; Fe3O4 (core)/ZrO2 (shell) nano particles (ZMPs)] as immunosensing probes. To design the immunoassay, the CEA antibody and O-phenylenediamine (OPD) were initially immobilized on a chitosan/nano gold composite membrane on a glassy carbon electrode (GCE/CS-nano Au), which was used for CEA recognition. Then, horseradish peroxidase (HRP)-labeled anti-CEA antibodies (HRP-CEA Ab2) were bound to the surface of the synthesized magnetic ZMP nanoparticles as signal tag. Thus, the sandwich-type immune complex could be formed between secondary antibody (Ab2) modified DNA/ZMPs nanochains tagged by HRP and GCE/CS-nano Au. Unlike conventional nanoparticle-based electrochemical immunoassays, the recognition elements of this immunoassay included both electron mediators and enzyme labels, which obviously simplifies the electrochemical measurement process. The sandwich-type immunoassay format was used for online formation of the immunocomplex of CEA captured in the detection cell with an external magnet. The electrochemical signals derived from HRP during the reduction of H2O2 with OPD as electron mediator were measured. The method displayed a high sensitivity for CEA detection in the range of 0.008–200 ng/mL, with a detection limit of 5 pg/mL (estimated at a signal-to-noise ratio of 3). The precision, reproducibility, and stability of the immunoassay were good. The use of the assay was evaluated with clinical serum samples, and the results were in excellent accordance with those obtained using the standard enzyme-linked immunosorbent assay (ELISA) method. Thus, the magnetic nanoparticle-based assay format is a promising approach for clinical applications, and it could be further developed for the detection of other biomarkers in cancer diagnosis

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Secure Information Flow by Model Checking Pushdown System

We propose an approach on model checking information flow for imperative language with procedures. We characterize our model with pushdown system, which has a stack of unbounded length that naturally models the execution of procedural programs. Because the type-based static analysis is sometimes too conservative and rejects safe program as ill-typed, we take a semantic-based approach by self-composing symbolic pushdown system and specifying noninterference with LTL formula. Then we verify this LTL-expressed property via model checker Moped. Except for overcoming the conservative characteristic of type-based approach, our motivation also includes the insufficient state of arts on precise information flow analysis under interprocedural setting. To remedy the inefficiency of model checking compared with type system, we propose both compact form and contracted form of self-composition. According to experimental results, they can greatly increase the efficiency of realistic verification. Our method provides flexibility on separating program abstraction from noninterference verification, thus could be expected to use on different programming languages. ? 2009 IEEE.EI

Hybrid Overlay Structure Based on Virtual Node

Current peer-to-peer architectures generally can be grouped into three categories: centralized architectures that utilize central directory servers to process queries, decentralized structured architectures that accurately build an underlying topology to support distributed hash table efficiently, and decentralized unstructured architectures that impose no structure on the topology and typically propagate queries to neighbors for searching. Aiming at integrating the flexibility of unstructured architectures with the regularity of structured architectures, we propose a hybrid overlay structure based on virtual node. Especially the hybrid architecture utilizes virtual nodes to build a distributed ring with random links. We can use the distributed ring to perform short jumps, and apply random links to long jumps. With our hybrid design, keyword searching, even multi-keyword searching, can be performed efficiently; both popular and rare keywords can be quickly located. Furthermore, our architecture is robust to the change of system scale, and it can work well with low maintenance cost in the dynamic environment.Computer Science, Hardware &amp; ArchitectureComputer Science, Information SystemsComputer Science, Theory &amp; MethodsEngineering, Electrical &amp; ElectronicTelecommunicationsEICPCI-S(ISTP)

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

Oil tea (Camellia oleifera) is one of the world&rsquo;s major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum&ndash;minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management

The Demographic Implication for Promoting Sponge City Initiatives in the Chinese Megacities: A Case of Wuhan

Urbanisation and ever-intensified rainstorms exacerbated urban waterlogging in some Chinese cities. In 2013, the Chinese government proposed a nationwide initiative, Sponge City, for managing the flood risk using the nature-based solution (NBS) approach. Pilot projects have been implemented among thirty selected cities, including Wuhan. Because the effectiveness of implementing NBS relies on the participation of the well-informed public, this study aims at identifying the factors affecting the awareness of the public about the Sponge City program. The viewpoint of people in Wuhan on urban floods and the Sponge City initiatives was surveyed among 1600 participants using a face-to-face questionnaire in mostly Wuchang area of Wuhan; more than 900 of them were further interviewed. The majority of participants, though recognising the threats from flooding, were lacking awareness and understanding of the Sponge City initiatives. The Chi-square analyses of association revealed that the level of awareness is affected by education, age and residential time; these demographic factors also affected their interpretation of the direct experiences of the water environment and governmental water management. To optimise communicating the relevant policy to the public, the content and the advertising tools for promoting Sponge City may need to be mindfully customised for targeted demographic groups