Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

by   Anisa Halimi, et al.

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, we propose a framework that verifies the correctness of the statistical test results that are conducted by a researcher while protecting individuals' privacy in the researcher's dataset. The researcher publishes the workflow of the conducted study, its output, and associated metadata. They keep the research dataset private while providing, as part of the metadata, a partial noisy dataset (that achieves local differential privacy). To check the correctness of the workflow output, a verifier makes use of the workflow, its metadata, and results of another statistical study (using publicly available datasets) to distinguish between correct statistics and incorrect ones. We use case the proposed framework in the genome-wide association studies (GWAS), in which the goal is to identify highly associated point mutations (variants) with a given phenotype. For evaluation, we use real genomic data and show that the correctness of the workflow output can be verified with high accuracy even when the aggregate statistics of a small number of variants are provided. We also quantify the privacy leakage due to the provided workflow and its associated metadata in the GWAS use-case and show that the additional privacy risk due to the provided metadata does not increase the existing privacy risk due to sharing of the research results. Thus, our results show that the workflow output (i.e., research results) can be verified with high confidence in a privacy-preserving way. We believe that this work will be a valuable step towards providing provenance in a privacy-preserving way while providing guarantees to the users about the correctness of the results.


page 1

page 2

page 3

page 4


I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies

Genome-wide Association Studies (GWASes) identify genomic variations tha...

Hardening X.509 Certificate Issuance using Distributed Ledger Technology

The security of cryptographic communication protocols that use X.509 cer...

Secure and Distributed Assessment of Privacy-Preserving Releases of GWAS

Genome-wide association studies (GWAS) identify correlations between the...

Testing Differential Privacy with Dual Interpreters

Applying differential privacy at scale requires convenient ways to check...

"Am I Private and If So, how Many?" - Communicating Privacy Guarantees of Differential Privacy with Risk Communication Formats

Decisions about sharing personal information are not trivial, since ther...

PeQES: A Platform for Privacy-enhanced Quantitative Empirical Studies

Empirical sciences and in particular psychology suffer a methodological ...

MIT SafePaths Card (MiSaCa): Augmenting Paper Based Vaccination Cards with Printed Codes

In this early draft, we describe a user-centric, card-based system for v...

Please sign up or login with your details

Forgot password? Click here to reset