PySEAL: A Python wrapper implementation of the SEAL homomorphic
  encryption library

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Abstract

Motivation: The ability to perform operations on encrypted data has a growing number of applications in bioinformatics, with implications for data privacy in health care and biosecurity. The SEAL library is a popular implementation of fully homomorphic encryption developed in C++ by Microsoft Research. Despite the advantages of C++, Python is a flexible and dominant programming language that enables rapid prototyping of bioinformatics pipelines. Results: In an effort to make homomorphic encryption accessible to a broader range of bioinformatics scientists and applications, we present a Python binding implementation of the popular homomorphic encryption library, SEAL, using pybind11. The software contains a Docker image to facilitate easy installation and execution of the SEAL build process. Availability: All code is publicly available at https://github.com/Lab41/PySEAL Contact: lab41@iqt.org Supplementary information: Supplementary information is available on the Lab41 GitHub.

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Identifying personal genomes by surname inference.

Melissa Gymrek, Amy McGuire, David Golan … (2013)

Sharing sequencing data sets without identifiers has become a common practice in genomics. Here, we report that surnames can be recovered from personal genomes by profiling short tandem repeats on the Y chromosome (Y-STRs) and querying recreational genetic genealogy databases. We show that a combination of a surname with other types of metadata, such as age and state, can be used to triangulate the identity of the target. A key feature of this technique is that it entirely relies on free, publicly accessible Internet resources. We quantitatively analyze the probability of identification for U.S. males. We further demonstrate the feasibility of this technique by tracing back with high probability the identities of multiple participants in public sequencing projects.