Novavita v3.26 opens a new era of genetic data privacy and confidential computing

 Novavita v3.26 opens a new era of genetic data privacy and confidential computing

In recent years, there are representatives of different dimensions in the construction of gene related databases in China, including the national gene bank. Other databases, such as life and health big data center, national genomics data encyclopedia, and proteomics integrated resource database, are databases for different types of genes. In addition, genome-wide association analysis, liquid biopsy technology in early screening of cancer, nucleic acid detection kit in Xinguan epidemic situation and various gene sequencing platforms are closely related to gene research. Human exploration of genes has never stopped.

With the advent of the era of big data, the importance of data will not only be reflected in the field of Internet. In the future, medical development, especially in gene detection and gene database establishment, will increasingly rely on data sharing, the most typical is genome-wide association analysis. Genome wide association study (GWAS) is to find out the sequence variation within the whole human genome, namely single nucleotide polymorphisms (SNPs), and to screen out the SNPs related to diseases, so as to help diagnose or prevent diseases. It is often used in the study of complex diseases, including cancer, diabetes and hypertension. This kind of disease is often affected by multiple genes and environmental factors, and each gene has a weak role alone, and there is often interaction between multiple genes and gene environment, so it is called complex disease. Using GWAS to study its genetic mechanism can not only help researchers to explore and understand the relevant mechanism of occurrence and development, but also help to develop new drugs, develop new therapies and carry out prevention work, so as to improve the overall national health level. Obviously, GWAS research is very dependent on the accumulation of a large number of gene data, however, this goal is often difficult to achieve. On the contrary, the most common problem and difficulty in GWAS research is that the sample size is too small. Although China has established a variety of multi-dimensional gene databases, most of the gene data in these gene banks exist alone, lacking of interaction and sharing, forming an island of data, which makes them unable to play their full value.

The most important reason for this phenomenon is the problem of data security. Due to the characteristics of genetic data, such as personal identification, disease risk prediction and other general data resources, once leaked, it will cause unpredictable losses to data providers. At the same time, these negative effects will spread to their blood relatives because they have similar gene fragments. Moreover, in recent years, many studies have shown that even if the data is de identified, there is still the risk of privacy information disclosure. Therefore, the great value contained in genetic data and the high risk in the process of data sharing make most of the holders of genetic data lose the willingness to share data and only want to hold the data in hand. Therefore, how to properly preserve and utilize these data, so as to avoid them becoming useless resources of tasteless food, but a pity to discard, has become a new challenge. Although there are many challenges, this seemingly insurmountable technical problem is a bright and smooth road for scientific research and development. If you want to go through it, you must overcome this obstacle, that is, to enable gene data to be shared safely.

As the pioneer and promoter of data privacy computing industry, this high-performance genomic data joint sharing and analysis platform based on privacy computing is a revolutionary and innovative medical biological big data privacy protection platform. Weiweis privacy computing platform adopts software and hardware encryption computing technology (such as multi-party secure computing, homomorphic encryption, trusted computing environment), and its main core is secure Federation learning technology, blockchain Traceability Technology and customizable hyper fusion infrastructure technology, forming its own unique privacy and confidentiality computing technology.

Among them, tee has relatively high execution efficiency. High processing capacity, through our algorithm optimization, can process massive data to meet the needs of specific business scenarios. Multi party security computing can better deal with two party data problems. Homomorphic encryption can have high resistance to quantum attacks and simple data processing capabilities. Cryptography technology is used to assist in data management, identity authentication and other contents. Blockchain is used to assist in business process management and audit management. Through the comprehensive utilization of the above technologies, the privacy and confidentiality computing platform of Weiwei can be used as a big data platform to break the data isolated island, establish cross industry, cross department and cross subject, and realize the data joint calculation of multi industry, multi department and multi center. At the same time, it can carry out multi center and multi-dimensional real-time big data analysis and calculation under the condition of complying with Chinas network security law and gdpr and other strict privacy protection laws and regulations, which can meet the high-standard privacy protection requirements of genetic data such as high-throughput, large-scale and high-sensitivity data types.

At present, this platform has passed the test of specific application scenarios, proving its great potential in protecting data privacy and promoting collaborative genomic research of different diseases. At the end of 2019, with the support of novavita platform, a joint accurate analysis of multi center rheumatic immune genomic data was carried out. This is also the first time in China that many hospitals carry out genome-wide association analysis without providing individual gene data for each participating hospital. The privacy sensitivity of gene data is much higher than that of other biomedical data (such as clinical data or medical image data), and data desensitization and de identification can not effectively prevent the leakage of sensitive genetic information of patients. Studies have shown that only dozens of statistically independent gene loci are needed to determine the identity of an individual. Therefore, in general, privacy security and cross center GWAS research can not be achieved at the same time, but with the technical support of novavita v3.26, not only can the two objectives be achieved at the same time, but also the results obtained by traditional methods can be ensured to be consistent. At present, the research results have been published in the top journals of bioinformatics.

Weiwei technology is the worlds leading provider of privacy computing services. The founding team began to study privacy computing in 2011, and published the first medical online federal learning paper in 2013. The team has more than 300 + works in the field of privacy computing, and is deeply engaged in the medical big data industry. It is the pioneer and promoter of medical big data privacy computing. It has taken the lead in launching more than ten cross domain and multi center medical big data privacy computing projects, providing a strong new infrastructure guarantee for the sharing and analysis of medical big data and the development of precision medicine. Weiwei technology is committed to developing a set of independent, secure and controllable privacy computing infrastructure platform for China, realizing the new computing paradigm of data available invisible and data immovable value dynamic, which enables multi-party data collaboration and calculation without exposing the original data and models. The company takes Weixin privacy and confidentiality computing architecture as the core, covering a series of products, such as novavita, novaeco, novagov, etc. The team has won the highest award in the annual medical information conference of the United States. Since 2014, the team has created the global idash privacy computing competition and participated in the formulation of several international and domestic privacy computing technology standards. Source: editor in charge of mass news: Chen Tiqiang_ NB6485