A Secured Proxy-Based Data Sharing Module in IoT Environments Using Blockchain.

Access and utilization of data are central to the cloud computing paradigm. With the advent of the Internet of Things (IoT), the tendency of data sharing on the cloud has seen enormous growth. With data sharing comes numerous security and privacy issues. In the process of ensuring data confidentiality and fine-grained access control to data in the cloud, several studies have proposed Attribute-Based Encryption (ABE) schemes, with Key Policy-ABE (KP-ABE) being the prominent one. Recent works have however suggested that the confidentiality of data is violated through collusion attacks between a revoked user and the cloud server. We present a secured and efficient Proxy Re-Encryption (PRE) scheme that incorporates an Inner-Product Encryption (IPE) scheme in which decryption of data is possible if the inner product of the private key, associated with a set of attributes specified by the data owner, and the associated ciphertext is equal to zero 0 . We utilize a blockchain network whose processing node acts as the proxy server and performs re-encryption on the data. In ensuring data confidentiality and preventing collusion attacks, the data are divided into two, with one part stored on the blockchain network and the other part stored on the cloud. Our approach also achieves fine-grained access control.

Blockchain-secure patient Digital Twin in healthcare using smart contracts.

Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.

Polymorphisms of the bovine DKK2 and their associations with body measurement traits and meat quality traits in Qinchuan cattle.

The objective of this research were to detect bovine Dickkopf 2 (DKK2) gene polymorphism and analyze their associations with body measurement traits (BMT) and meat quality traits (MQT) of animals. Blood samples were taken from a total of 541 Qinchuan cattle aged from 18 to 24 months. Polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) was employed to find out DKK2 single-polymorphism nucleotide (SNPs) and to explore their possible association with BMT and MQT. Sequence analysis of DKK2 gene revealed 2 SNPs (C29 T and A169C) in 5' untranslated region (5'UTR) of exon 1.C29T and A164T SNPs are both synonymous mutation, which showed 2 genotypes namely (CC, CT) and (AA and AC), respectively. Association analysis of polymorphism with body measurement and meat quality traits at the two locus showed that there were significant effects on CT, BL, RL, PBW, BFT, LMA, and IFC. These results suggest that the DKK2 gene might have potential effects on BMT and MQT in Qinchuan cattle population and could be used for marker-assisted selection.

The polymorphisms of LYRM1 gene and their association with body measurement and ultrasound traits of Qinchuan cattle.

Body measurement and meat quality traits which play important roles in the assessment of productivity and economy in cattle were influenced by genes and environmental factors. Latest studies showed that LYR motif containing 1 (LYRM1) may be involved in influencing fatness deposition in animals. The objective of this study was to detect bovine LYRM1 gene polymorphism and analyze its association with body measurement and meat quality traits of cattle. Blood samples were taken from a total of 404 Qinchuan cattle aged from 18-24 months. Created restriction site-polymerase chain reaction-restriction fragment length polymorphism (CRS-PCR-RFLP) and DNA sequencing were used to find out LYRM1 single polymorphism nucleotide (SNPs). Sequence analysis of LYRM1 gene revealed two SNPs (g.165 C > A, g.193 A > G) in 3' untranslated region (3'UTR) of exon 3. And g.165 C > A showed two genotypes namely AC and CC while g.193 A > G showed three genotypes: AA, AG and GG. Analysis results showed that there were significant associations between polymorphism of these two and body measurement and meat quality traits in Qinchuan cattle population. Based on the results obtained from this study, it is inferred that LYRM1 gene may have potential effects on body measurement and meat quality traits in Qinchuan cattle population and could be used for marker-assisted selection.

A novel blind separation method in magnetic resonance images.

A novel global search algorithm based method is proposed to separate MR images blindly in this paper. The key point of the method is the formulation of the new matrix which forms a generalized permutation of the original mixing matrix. Since the lowest entropy is closely associated with the smooth degree of source images, blind image separation can be formulated to an entropy minimization problem by using the property that most of neighbor pixels are smooth. A new dataset can be obtained by multiplying the mixed matrix by the inverse of the new matrix. Thus, the search technique is used to searching for the lowest entropy values of the new data. Accordingly, the separation weight vector associated with the lowest entropy values can be obtained. Compared with the conventional independent component analysis (ICA), the original signals in the proposed algorithm are not required to be independent. Simulation results on MR images are employed to further show the advantages of the proposed method.

Landmark matching based retinal image alignment by enforcing sparsity in correspondence matrix.

Retinal image alignment is fundamental to many applications in diagnosis of eye diseases. In this paper, we address the problem of landmark matching based retinal image alignment. We propose a novel landmark matching formulation by enforcing sparsity in the correspondence matrix and offer its solutions based on linear programming. The proposed formulation not only enables a joint estimation of the landmark correspondences and a predefined transformation model but also combines the benefits of the softassign strategy (Chui and Rangarajan, 2003) and the combinatorial optimization of linear programming. We also introduced a set of reinforced self-similarities descriptors which can better characterize local photometric and geometric properties of the retinal image. Theoretical analysis and experimental results with both fundus color images and angiogram images show the superior performances of our algorithms to several state-of-the-art techniques.

Landmark matching based automatic retinal image registration with linear programming and self-similarities.

In this paper, we address the problem of landmark matching based retinal image registration. Two major contributions render our registration algorithm distinguished from many previous methods. One is a novel landmark-matching formulation which enables not only a joint estimation of the correspondences and transformation model but also the optimization with linear programming. The other contribution lies in the introduction of a reinforced self-similarities descriptor in characterizing the local appearance of landmarks. Theoretical analysis and a series of preliminary experimental results show both the effectiveness of our optimization scheme and the high differentiating ability of our features.