Related papers: Enabling Privacy-Preserving GWAS in Heterogeneous …
Traditional statistical methods for confidentiality protection of statistical databases do not scale well to deal with GWAS (genome-wide association studies) databases especially in terms of guarantees regarding protection from linkage to…
The protection of privacy of individual-level information in genome-wide association study (GWAS) databases has been a major concern of researchers following the publication of "an attack" on GWAS data by Homer et al. (2008) Traditional…
Genome-wide association studies (GWAS) are an essential tool in biomedical research for identifying genetic factors linked to health and disease. However, publicly releasing GWAS summary statistics poses well-recognized privacy risks,…
Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, careless sharing of GWAS statistics might give rise to privacy…
Genome-wide association studies are pivotal in understanding the genetic underpinnings of complex traits and diseases. Collaborative, multi-site GWAS aim to enhance statistical power but face obstacles due to the sensitive nature of genomic…
As genomic research has grown increasingly popular in recent years, dataset sharing has remained limited due to privacy concerns. This limitation hinders the reproducibility and validation of research outcomes, both of which are essential…
Recent studies demonstrate that effective healthcare can benefit from using the human genomic information. For instance, analysis of tumor genomes has revealed 140 genes whose mutations contribute to cancer. As a result, many institutions…
With the increasing affordability and availability of patient data, hospitals tend to outsource their data to cloud service providers (CSPs) for the purpose of storage and analytics. However, the concern of data privacy significantly limits…
Genome-wide association studies (GWAS) identify correlations between the genetic variants and an observable characteristic such as a disease. Previous works presented privacy-preserving distributed algorithms for a federation of genome data…
Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can not only identify an…
Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. Workflow systems model and record provenance describing the steps performed to obtain the final results of a computation. In this work,…
One way of investigating how genes affect human traits would be with a genome-wide association study (GWAS). Genetic markers, known as single-nucleotide polymorphism (SNP), are used in GWAS. This raises privacy and security concerns as…
Personalized health analytics increasingly rely on population benchmarks to provide contextual insights such as ''How do I compare to others like me?'' However, cohort-based aggregation of health data introduces nontrivial privacy risks,…
Differential privacy is known to protect against threats to validity incurred due to adaptive, or exploratory, data analysis -- even when the analyst adversarially searches for a statistical estimate that diverges from the true value of the…
Users of a personalised recommendation system face a dilemma: recommendations can be improved by learning from data, but only if the other users are willing to share their private information. Good personalised predictions are vitally…
The increasing prevalence of high-dimensional data across various applications has raised significant privacy concerns in statistical inference. In this paper, we propose a differentially private integrated statistic for testing…
Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including…
Genome-wide association studies (GWAS) have identified hundreds of loci at very stringent levels of statistical significance across many different human traits. However, it is now clear that very large samples (n~10^4-10^5) are needed to…
Progress in Whole Genome Sequencing (WGS) will soon allow a large number of individuals to have their genome fully sequenced. This lays the foundations to improve modern healthcare, enabling a new era of personalized medicine where…
Objective: To enable privacy-preserving learning of high quality generative and discriminative machine learning models from distributed electronic health records. Methods and Results: We describe general and scalable strategy to build…