Implementing Privacy-Preserving Genotype Analysis with Consideration for Population Stratification

In bioinformatics, genome-wide association studies (GWAS) are used to detect associations between single-nucleotide polymorphisms (SNPs) and phenotypic traits such as diseases. Significant differences in SNP counts between case and control groups can signal association between variants and phenotypic traits. Most traits are affected by multiple genetic locations. To detect these subtle associations, bioinformaticians need access to more heterogeneous data. Regulatory restrictions in cross-border health data exchange have created a surge in research on privacy-preserving solutions, including secure computing techniques. However, in studies of such scale, one must account for population stratification, as under- and over-representation of sub-populations can lead to spurious associations. We improve on the state of the art of privacy-preserving GWAS methods by showing how to adapt principal component analysis (PCA) with stratification control (EIGENSTRAT), FastPCA, EMMAX and the genomic control algorithm for secure computing. We implement these methods using secure computing techniques—secure multi-party computation (MPC) and trusted execution environments (TEE). Our algorithms are the most complex ones at this scale implemented with MPC. We present performance benchmarks and a security and feasibility trade-off discussion for both techniques.

A Conceptual Architecture in Decentralizing Computing, Storage, and Networking Aspect of IoT Infrastructure

Since the inception of the Internet of Things (IoT), we have adopted centralized architecture for decades. With the vastly growing number of IoT devices and gateways, this architecture struggles to cope with the high demands of state-of-the-art IoT services, which require scalable and responsive infrastructure. In response, decentralization becomes a considerable interest among IoT adopters. Following a similar trajectory, this paper introduces an IoT architecture re-work that enables three spheres of IoT workflows (i.e., computing, storage, and networking) to be run in a distributed manner. In particular, we employ the blockchain and smart contract to provide a secure computing platform. The distributed storage network maintains the saving of IoT raw data and application data. The software-defined networking (SDN) controllers and SDN switches exist in the architecture to provide connectivity across multiple IoT domains. We envision all of those services in the form of separate yet integrated peer-to-peer (P2P) overlay networks, which IoT actors such as IoT domain owners, IoT users, Internet Service Provider (ISP), and government can cultivate. We also present several IoT workflow examples showing how IoT developers can adapt to this new proposed architecture. Based on the presented workflows, the IoT computing can be performed in a trusted and privacy-preserving manner, the IoT storage can be made robust and verifiable, and finally, we can react to the network events automatically and quickly. Our discussions in this paper can be beneficial for many people ranging from academia, industries, and investors that are interested in the future of IoT in general.

“Bodies not templates”: Contesting dominant algorithmic imaginaries

Through an array of technological solutions and awareness-raising initiatives, civil society mobilizes against an onslaught of surveillance threats. What alternative values, practices, and tactics emerge from the grassroots which point toward other ways of being in the datafied society? Conversing with critical data studies, science and technology studies, and surveillance studies, this article looks at how dominant imaginaries of datafication are reconfigured and responded to by groups of people dealing directly with their harms and risks. Building on practitioner interviews and participant observation in digital rights events and surveying projects intervening in three critical technological issues of our time—the challenges of digitally secure computing, the Internet of Things, and the threat of widespread facial recognition—this article investigates social justice activists, human rights defenders, and progressive technologists as they try to flip dominant algorithmic imaginaries. In so doing, the article contributes to our understanding of how individuals and social groups make sense of the challenges of datafication from the bottom-up.

Scaling Digital Images Using Homomorphic Encryption

Since time immemorial, cryptography has provided secure transmission of information in an insecure environment, keeping the data secret. Not so long ago the homomorphic cryptography began to actively develop. Its distinctive feature is that this type of cryptography allows you to process encrypted data without their preliminary decryption in such a way that the result of operations on encrypted data is equivalent, after decryption, to the result of operations on open data. Because of these features, homomorphic encryption can be effectively used in various cloud services to perform secure computing and secure image processing. At the same time, it is guaranteed that no one will have open data, even the service that performs the calculations. Purpose of the work: development of methods and tools for homomorphic encryption that allow performing homomorphic implementation of image processing algorithms. Research methods: analysis of possible implementations of digital image processing using homomorphic encryption, analysis of existing problems of performing a homomorphic implementation for image processing algorithms. Results: a method for homomorphic comparison of bits and numbers presented as an array of bits is proposed; a homomorphic implementation of the EPX image resizing algorithm is proposed; the complexity of the operation is analyzed when one pixel of the original image is enlarged using the proposed method; the analysis results are presented.