Genome Reconstruction Attacks Against Genomic Data-Sharing Beacons

Multidisciplinary molecular consultation increases the diagnosis of pediatric epileptic encephalopathy and neurodevelopmental disorders

BACKGROUND

Epilepsy (EP) is a common neurological disease in which 70-80% are thought to have a genetic cause. In patients with epilepsy, neurodevelopmental delay (NDD) was prevalent. Next generation of sequencing has been widely used in diagnosing EP/NDD. However, the diagnostic yield remains to be 40%-50%. Many reanalysis pipelines and software have been developed for automated reanalysis and decision making for the diseases. Nevertheless, it is a highly challenging task for smaller genetic centers or a routine pediatric practice. To address the clinical and genetic "diagnostic odyssey," we organized a Multidisciplinary Molecular Consultation (MMC) team for molecular consultation for 202 children with EP/NDD patients referred by lower level hospitals.

METHODS

All the patients had undergone an aligned and sequential consultations and discussions by a "triple reanalysis" procedure by clinical, genetic specialists, and researchers.

RESULTS

Among the 202 cases for MMC, we totally identified 47 cases (23%) harboring causative variants in 24 genes and 15 chromosomal regions after the MMC. In the 15 cases with positive CNVs, 3 cases harbor the deletions or duplications in 16p11.2, and 2 cases for 1p36. The bioinformatical reanalysis revealed 47 positive cases, in which 12 (26%) were reported to be negative, VUS or incorrectly positive in pre-MMC reports. Additionally, among 87 cases with negative cases, 4 (5%) were reported to be positive in pre-MMC reports.

CONCLUSION

We established a workflow allowing for a "one-stop" collaborative assessments by experts of multiple fields and helps for correct the diagnosis of cases with falsenegative and -positive and VUS genetic reports and may have significant influences for intervention, prevention and genetic counseling of pediatric epilepsy and neurodevelopmental disorders.

Privacy-aware estimation of relatedness in admixed populations

BACKGROUND

Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization.

RESULTS

Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352.

CONCLUSIONS

Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness of individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations.

SHORT ABSTRACT

Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.

Genome Privacy and Trust

Genomics data are important for advancing biomedical research, improving clinical care, and informing other disciplines such as forensics and genealogy. However, privacy concerns arise when genomic data are shared. In particular, the identifying nature of genetic information, its direct relationship to health status, and the potential financial harm and stigmatization posed to individuals and their blood relatives call for a survey of the privacy issues related to sharing genetic and related data and potential solutions to overcome these issues. In this work, we provide an overview of the importance of genomic privacy, the information gleaned from genomics data, the sources of potential private information leakages in genomics, and ways to preserve privacy while utilizing the genetic information in research. We discuss the relationship between trust in the scientific community and protecting privacy, illuminating a future roadmap for data sharing and study participation.

Genomic Data Sharing under Dependent Local Differential Privacy

Privacy-preserving genomic data sharing is prominent to increase the pace of genomic research, and hence to pave the way towards personalized genomic medicine. In this paper, we introduce (ϵ, T)-dependent local differential privacy (LDP) for privacy-preserving sharing of correlated data and propose a genomic data sharing mechanism under this privacy definition. We first show that the original definition of LDP is not suitable for genomic data sharing, and then we propose a new mechanism to share genomic data. The proposed mechanism considers the correlations in data during data sharing, eliminates statistically unlikely data values beforehand, and adjusts the probability distributions for each shared data point accordingly. By doing so, we show that we can avoid an attacker from inferring the correct values of the shared data points by utilizing the correlations in the data. By adjusting the probability distributions of the shared states of each data point, we also improve the utility of shared data for the data collector. Furthermore, we develop a greedy algorithm that strategically identifies the processing order of the shared data points with the aim of maximizing the utility of the shared data. Our evaluation results on a real-life genomic dataset show the superiority of the proposed mechanism compared to the randomized response mechanism (a widely used technique to achieve LDP).

Using game theory to thwart multistage privacy intrusions when sharing data

Person-specific biomedical data are now widely collected, but its sharing raises privacy concerns, specifically about the re-identification of seemingly anonymous records. Formal re-identification risk assessment frameworks can inform decisions about whether and how to share data; current techniques, however, focus on scenarios where the data recipients use only one resource for re-identification purposes. This is a concern because recent attacks show that adversaries can access multiple resources, combining them in a stage-wise manner, to enhance the chance of an attack’s success. In this work, we represent a re-identification game using a two-player Stackelberg game of perfect information, which can be applied to assess risk, and suggest an optimal data sharing strategy based on a privacy-utility tradeoff. We report on experiments with large-scale genomic datasets to show that, using game theoretic models accounting for adversarial capabilities to launch multistage attacks, most data can be effectively shared with low re-identification risk.