Design and development of learning model for compression and processing of deoxyribonucleic acid genome sequence

Owing to the substantial volume of human genome sequence data files (from 30-200 GB exposed) Genomic data compression has received considerable traction and storage costs are one of the major problems faced by genomics laboratories. This involves a modern technology of data compression that reduces not only the storage but also the reliability of the operation. There were few attempts to solve this problem independently of both hardware and software. A systematic analysis of associations between genes provides techniques for the recognition of operative connections among genes and their respective yields, as well as understandings into essential biological events that are most important for knowing health and disease phenotypes. This research proposes a reliable and efficient deep learning system for learning embedded projections to combine gene interactions and gene expression in prediction comparison of deep embeddings to strong baselines. In this paper we preform data processing operations and predict gene function, along with gene ontology reconstruction and predict the gene interaction. The three major steps of genomic data compression are extraction of data, storage of data, and retrieval of the data. Hence, we propose a deep learning based on computational optimization techniques which will be efficient in all the three stages of data compression

Sociotechnical Safeguards for Genomic Data Privacy

Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of genomic data that are collected, used and shared. This state of affairs raises new and challenging concerns for personal privacy, both legally and technically. This Review appraises existing and emerging threats to genomic data privacy and discusses how well current legal frameworks and technical safeguards mitigate these concerns. It concludes with a discussion of remaining and emerging challenges and illustrates possible solutions that can balance protecting privacy and realizing the benefits that result from the sharing of genetic information

Secure, privacy-preserving and practical collaborative Genome-Wide Association Studies

Understanding the interplay between genomics and human health is a crucial step for the advancement and development of our society. Genome-Wide Association Study (GWAS) is one of the most popular methods for discovering correlations between genomic variations associated with a particular phenotype (i.e., an observable trait such as a disease). Leveraging genome data from multiple institutions worldwide nowadays is essential to produce more powerful findings by operating GWAS at larger scale. However, this raises several security and privacy risks, not only in the computation of such statistics, but also in the public release of GWAS results. To that extent, several solutions in the literature have adopted cryptographic approaches to allow secure and privacy-preserving processing of genome data for federated analysis. However, conducting federated GWAS in a secure and privacy-preserving manner is not enough since the public releases of GWAS results might be vulnerable to known genomic privacy attacks, such as recovery and membership attacks. The present thesis explores possible solutions to enable end-to-end privacy-preserving federated GWAS in line with data privacy regulations such as GDPR to secure the public release of the results of Genome-Wide Association Studies (GWASes) that are dynamically updated as new genomes become available, that might overlap with their genomes and considered locations within the genome, that can support internal threats such as colluding members in the federation and that are computed in a distributed manner without shipping actual genome data. While achieving these goals, this work created several contributions described below. First, the thesis proposes DyPS, a Trusted Execution Environment (TEE)-based framework that reconciles efficient and secure genome data outsourcing with privacy-preserving data processing inside TEE enclaves to assess and create private releases of dynamic GWAS. In particular, DyPS presents the conditions for the creation of safe dynamic releases certifying that the theoretical complexity of the solution space an external probabilistic polynomial-time (p.p.t.) adversary or a group of colluders (up to all-but-one parties) would need to infer when launching recovery attacks on the observation of GWAS statistics is large enough. Besides that, DyPS executes an exhaustive verification algorithm along with a Likelihood-ratio test to measure the probability of identifying individuals in studies. Thus, also protecting individuals against membership inference attacks. Only safe genome data (i.e., genomes and SNPs) that DyPS selects are further used for the computation and release of GWAS results. At the same time, the remaining (unsafe) data is kept secluded and protected inside the enclave until it eventually can be used. Our results show that if dynamic releases are not improperly evaluated, up to 8% of genomes could be exposed to genomic privacy attacks. Moreover, the experiments show that DyPS’ TEE-based architecture can accommodate the computational resources demanded by our algorithms and present practical running times for larger-scale GWAS. Secondly, the thesis offers I-GWAS that identifies the new conditions for safe releases when considering the existence of overlapping data among multiple GWASes (e.g., same individuals participating in several studies). Indeed, it is shown that adversaries might leverage information of overlapping data to make both recovery and membership attacks feasible again (even if they are produced following the conditions for safe single-GWAS releases). Our experiments show that up to 28.6% of genetic variants of participants could be inferred during recovery attacks, and 92.3% of these variants would enable membership attacks from adversaries observing overlapping studies, which are withheld by I-GWAS. Lastly yet importantly, the thesis presents GenDPR, which encompasses extensions to our protocols so that the privacy-verification algorithms can be conducted distributively among the federation members without demanding the outsourcing of genome data across boundaries. Further, GenDPR can also cope with collusion among participants while selecting genome data that can be used to create safe releases. Additionally, GenDPRproduces the same privacy guarantees as centralized architectures, i.e., it correctly identifies and selects the same data in need of protection as with centralized approaches. In the end, the thesis presents a homogenized framework comprising DyPS, I-GWAS and GenDPR simultaneously. Thus, offering a usable approach for conducting practical GWAS. The method chosen for protection is of a statistical nature, ensuring that the theoretical complexity of attacks remains high and withholding releases of statistics that would impose membership inference risks to participants using Likelihood-ratio tests, despite adversaries gaining additional information over time, but the thesis also relates the findings to techniques that can be leveraged to protect releases (such as Differential Privacy). The proposed solutions leverage Intel SGX as Trusted Execution Environment to perform selected critical operations in a performant manner, however, the work translates equally well to other trusted execution environments and other schemes, such as Homomorphic Encryption

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby ???tag??? variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation

Designing Data Spaces

This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty

Neolithic land-use in the Dutch wetlands: estimating the land-use implications of resource exploitation strategies in the Middle Swifterbant Culture (4600-3900 BCE)

The Dutch wetlands witness the gradual adoption of Neolithic novelties by foraging societies during the Swifterbant period. Recent analyses provide new insights into the subsistence palette of Middle Swifterbant societies. Small-scale livestock herding and cultivation are in evidence at this time, but their importance if unclear. Within the framework of PAGES Land-use at 6000BP project, we aim to translate the information on resource exploitation into information on land-use that can be incorporated into global climate modelling efforts, with attention for the importance of agriculture. A reconstruction of patterns of resource exploitation and their land-use dimensions is complicated by methodological issues in comparing the results of varied recent investigations. Analyses of organic residues in ceramics have attested to the cooking of aquatic foods, ruminant meat, porcine meat, as well as rare cases of dairy. In terms of vegetative matter, some ceramics exclusively yielded evidence of wild plants, while others preserve cereal remains. Elevated δ15N values of human were interpreted as demonstrating an important aquatic component of the diet well into the 4th millennium BC. Yet recent assays on livestock remains suggest grazing on salt marshes partly accounts for the human values. Finally, renewed archaeozoological investigations have shown the early presence of domestic animals to be more limited than previously thought. We discuss the relative importance of exploited resources to produce a best-fit interpretation of changing patterns of land-use during the Middle Swifterbant phase. Our review combines recent archaeological data with wider data on anthropogenic influence on the landscape. Combining the results of plant macroremains, information from pollen cores about vegetation development, the structure of faunal assemblages, and finds of arable fields and dairy residue, we suggest the most parsimonious interpretation is one of a limited land-use footprint of cultivation and livestock keeping in Dutch wetlands between 4600 and 3900 BCE.NWOVidi 276-60-004Human Origin