A fully homomorphism encryption scheme based on LWR

We believe that isomorphic encryption technology can provide strong technical support for users’ privacy protection in a distributed computing environment. There are three types of quasi-homomorphism encryption methods: partial homomorphism encryption, shallow homomorphism encryption, and full homomorphism encryption. homomorphism encryption methods have important applications for ciphertext data computing in distributed computing environments, such as secure cloud computing, fee computing, and remote file storage ciphertext retrieval. It is pointed out that the construction of the homomorphism encryption method is still in the theoretical stage and cannot be used for real high-density data calculation problems. How to design (natural) isomorphic encryption schemes according to algebraic systems is still a challenging research. This question discusses the problem of Learning With Rounding (LWR). Based on the difficulty of LWR, multiple IDs, and attribute categories, a fully homomorphism encryption method corresponding to an ID is proposed. In this paper, in order to reflect the effectiveness of the proposed method, we propose a homomorphism encryption technology based on the password search attribute.

Privacy-preserving inpainting for outsourced image

In this article, a framework of privacy-preserving inpainting for outsourced image and an encrypted-image inpainting scheme are proposed. Different with conventional image inpainting in plaintext domain, there are two entities, that is, content owner and image restorer, in our framework. Content owner first encrypts his or her damaged image for privacy protection and outsources the encrypted, damaged image to image restorer, who may be a cloud server with powerful computation capability. Image restorer performs inpainting in encrypted domain and sends the inpainted and encrypted image back to content owner or authorized receiver, who can acquire final inpainted result in plaintext domain through decryption. In our encrypted-image inpainting scheme, with the assist of Johnson–Lindenstrauss transform that can preserve Euclidean distance between two vectors before and after encryption, the best-matching block with the smallest distance to current block can be found and utilized for patch filling in Paillier-encrypted image. To eliminate mosaic effect after decryption, weighted mean filtering in encrypted domain is conducted with Paillier homomorphic properties. Experimental results show that our privacy-preserving inpainting framework can be effectively applied in secure cloud computing, and the proposed encrypted-image inpainting scheme achieves comparable visual quality of inpainted results with some typical inpainting schemes in plaintext domain.

An overview of biomedical platforms for managing research data

Biomedical platforms provide the hardware and software to securely ingest, process, validate, curate, store, and share data. Many large-scale biomedical platforms use secure cloud computing technology for analyzing, integrating, and storing phenotypic, clinical, and genomic data. Several web-based platforms are available for researchers to access services and tools for biomedical research. The use of bio-containers can facilitate the integration of bioinformatics software with various data analysis pipelines. Adoption of Common Data Models, Common Data Elements, and Ontologies can increase the likelihood of data reuse. Managing biomedical Big Data will require the development of strategies that can efficiently leverage public cloud computing resources. The use of the research community developed standards for data collection can foster the development of machine learning methods for data processing and analysis. Increasingly platforms will need to support the integration of data from multiple disease area research.