Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency

Recently, several practical attacks raised serious concerns over the security of searchable encryption. The attacks have brought emphasis on forward privacy, which is the key concept behind solutions to the adaptive leakage-exploiting attacks, and will very likely to become mandatory in the design of new searchable encryption schemes. For a long time, forward privacy implies inefficiency and thus most existing searchable encryption schemes do not support it. Very recently, Bost (CCS 2016) showed that forward privacy can be obtained without inducing a large communication overhead. However, Bost's scheme is constructed with a relatively inefficient public key cryptographic primitive, and has a poor I/O performance. Both of the deficiencies significantly hinder the practical efficiency of the scheme, and prevent it from scaling to large data settings. To address the problems, we first present FAST, which achieves forward privacy and the same communication efficiency as Bost's scheme, but uses only symmetric cryptographic primitives. We then present FASTIO, which retains all good properties of FAST, and further improves I/O efficiency. We implemented the two schemes and compared their performance with Bost's scheme. The experiment results show that both our schemes are highly efficient, and FASTIO achieves a much better scalability due to its optimized I/O

3-D KINETIC CHARACTERISTICS OF OBESE CHILDREN AND NORMAL WEIGHT CHILDREN DURING NORMAL WALKING

Previous researches on obese children walking gait have been mainly focused on the kinematic characteristics, and little focused on the kinetic characteristics. The aim of this study is to identify and compare the kinetic characteristics of the walking gait of obese children with those of normal weight children

We Can Make Mistakes: Fault-tolerant Forward Private Verifiable Dynamic Searchable Symmetric Encryption

Verifiable Dynamic Searchable Symmetric Encryption (VDSSE) enables users to securely outsource databases (document sets) to cloud servers and perform searches and updates. The verifiability property prevents users from accepting incorrect search results returned by a malicious server. However, we discover that the community currently only focuses on preventing malicious behavior from the server but ignores incorrect updates from the client, which are very likely to happen since there is no record on the client to check. Indeed most existing VDSSE schemes are not sufficient to tolerate incorrect updates from the client. For instance, deleting a nonexistent keyword-identifier pair can break their correctness and soundness. In this paper, we demonstrate the vulnerabilities of a type of existing VDSSE schemes that fail them to ensure correctness and soundness properties on incorrect updates. We propose an efficient fault-tolerant solution that can consider any DSSE scheme as a black-box and make them into a fault-tolerant VDSSE in the malicious model. Forward privacy is an important property of DSSE that prevents the server from linking an update operation to previous search queries. Our approach can also make any forward secure DSSE scheme into a fault-tolerant VDSSE without breaking the forward security guarantee. In this work, we take FAST [1] (TDSC 2020), a forward secure DSSE, as an example, implement a prototype of our solution, and evaluate its performance. Even when compared with the previous fastest forward private construction that does not support fault tolerance, the experiments show that our construction saves 9× client storage and has better search and update efficiency

Temperature-controlled electrospinning of EVOH nanofibre mats encapsulated with Ag, CuO, and ZnO particles for skin wound dressing

To treat skin burns, a new wound dressing, nanofibre mats with metal or metal oxide nanoparticles (Ag, CuO, and ZnO), was fabricated using the electrospinning technique. During a therapeutic process, the antibacterial ability and bio-compatibility of a new dressing material are of major concern. To expound the characteristics of ethylene vinyl alcohol (EVOH) nanofibre mats encapsulated with metal or metal oxide nanoparticles, denoted as Ag-EVOH, CuO-EVOH, and ZnO-EVOH, for use as new wound dressing materials, we investigated the suitable processing parameters to fabricate these materials, such as the voltage, tip-to-collector distance, concentration of the solution, and effect of environmental temperature. The antibacterial ability and bio-compatibility of Ag-EVOH, CuO-EVOH, and ZnO-EVOH were then tested and quantified. The outcomes show that the most suitable temperature for the fabrication of the materials is 40 °C (±3 °C). The antibacterial experiment results indicate that 0.08 g/ml of metal/metallic oxide shows the highest antibacterial ability toward Staphylococcus aureus. Furthermore, the largest diameters of the bacteriostatic loops of the three types of nanofibre mats, i.e. Ag-EVOH, CuO-EVOH, and ZnO-EVOH, are 5.89, 5.21, and 4.12 mm, respectively. Finally, the cell proliferations on the three nanofibre mats show a similar growth trend

Larotrectinib treatment for infantile fibrosarcoma in newborns: a case report and literature review

Infantile fibrosarcoma (IFS) is a rare tumor in childhood characterized by a single, localized, painless mass that grows rapidly but has a relatively indolent biological behavior and a favorable prognosis. Eighty-five percent of infantile fibrosarcomas are associated with t (12;15) (p13;25) chromosomal translocation resulting in ETV6-NTRK3 gene fusion, which provides the target for targeted therapy. Here, we report a case of IFS in a newborn with a mass in the left lower extremity confirmed by imaging, histopathological examination, tissue FISH testing, and high-throughput sequencing to detect gene rearrangement. Based on gene fusion targeted drug testing results, the patient was treated with standard doses of larotrectinib, resulting in significant mass shrinkage with no adverse effects, demonstrating the treatment effect of targeted therapy. This case provides a reference for using larotrectinib in newborns with IFS

Phosphate Assay Kit in One Cell for Electrochemical Detection of Intracellular Phosphate Ions at Single Cells

In this paper, phosphate assay kit in one cell is realized for the electrochemical detection of intracellular phosphate ions at single cells. The components of the phosphate assay kit, including maltose phosphorylase, maltose, mutarotase, and glucose oxidase, are electrochemically injected into a living cell through a nanometer-sized capillary with the ring electrode at the tip. These components react with phosphate ions inside the cell to generate hydrogen peroxide that is electrochemically oxidized at the ring electrode for the qualification of intracellular phosphate ions. An average 1.7 nA charge was collected from eight individual cells, suggesting an intracellular phosphate concentration of 2.1 mM. The establishment in the electrochemical measurement of phosphate ions provides a special strategy to monitor the fluctuation of intracellular phosphate at single cells, which is significant for the future investigation of phosphate signal transduction pathway

ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation

Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to improve model’s performance further. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fully-supervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer