Genome sequencing has rapidly advanced in the last decade, making it easier for anyone to obtain digital genomes at low costs from companies such as Helix, MyHeritage, and 23andMe. Companies now offer their services in a direct-to-consumer (DTC) model without the intervention of a medical institution. Thereby, providing people with direct services for paternity testing, ancestry testing and disease susceptibility testing (DST) to infer diseases' predisposition. Genome analyses are partly motivated by curiosity and people often want to partake without fear of privacy invasion. Existing privacy protection solutions for DST adopt cryptographic techniques to protect the genome of a patient from the party responsible for computing the analysis. Said techniques include homomorphic encryption, which can be computationally expensive and could take minutes for only a few single-nucleotide polymorphisms (SNPs). A predominant approach is a solution that computes DST over encrypted data, but the design depends on a medical unit and exposes test results of patients to the medical unit, making the design uncomfortable for privacy-aware individuals. Hence it is pertinent to have an efficient privacy-preserving DST solution with a DTC service. We propose a novel DTC model that protects the privacy of SNPs and prevents leakage of test results to any other party save for the genome owner. Conversely, we protect the privacy of the algorithms or trade secrets used by the genome analyzing companies. Our work utilizes a secure obfuscation technique in computing DST, eliminating expensive computations over encrypted data. Our approach significantly outperforms existing state-of-the-art solutions in runtime and scales linearly for equivalent levels of security. As an example, computing DST for 10,000 SNPs requires approximately 96 milliseconds on commodity hardware. With this efficient and privacy-preserving solution which is also simulation-based secure, we open possibilities for performing genome analyses on collectively shared data resources.

In a society where digital data has become ubiquitous and has been projected to continue in this trajectory for the foreseeable future, machine learning has become a dependable tool to aid in analyzing these big datasets. However, where the data or machine learning algorithms are considered to be privacy-sensitive, one is then faced with the challenge of preserving the utility of machine learning in a privacy-preserving setting. In this paper, we focus on a use case where decentralized parties have privately owned machine learning algorithms, and would want to jointly generate a public model while not violating the privacy of their individual models, and data. We present ECONoMy: a privacy-preserving protocol that supports collaborative learning using an ensemble technique. Set in an honest-but-curious security model, ECONoMy is lightweight and provides efficiency and privacy in settings with large participant such as with IoT devices.

Genetic data are important dataset utilised in genetic epidemiology to investigate biologically coded information within the human genome. Enormous research has been delved into in recent years in order to fully sequence and understand the genome. Personalised medicine, patient response to treatments and relationships between specific genes and certain characteristics such as phenotypes and diseases, are positive impacts of studying the genome, just to mention a few. The sensitivity, longevity and non-modifiable nature of genetic data make it even more interesting, consequently, the security and privacy for the storage and processing of genomic data beg for attention. A common activity carried out by geneticists is the association analysis between allele-allele, or even a genetic locus and a disease. We demonstrate the use of cryptographic techniques such as homomorphic encryption schemes and multiparty computations, how such analysis can be carried out in a privacy friendly manner. We compute a 3 × 3 contingency table, and then, genome analyses algorithms such as linkage disequilibrium (LD) measures, all on the encrypted domain. Our computation guarantees privacy of the genome data under our security settings, and provides up to 98:4% improvement, compared to an existing solution.