Work

On the privacy risks of sharing clinical proteomics data.

Year: 2016

Type: article

Abstract: Although the privacy issues in human genomic studies are well known, the privacy risks in clinical proteomic data have not been thoroughly studied. As a proof of concept, we reported a comprehensive analysis of the privacy risks in clinical proteomic data. It showed that a small number of peptides carrying the minor alleles (referred to as the minor allelic peptides) at non-synonymous single nucleotide polymorphism (nsSNP) sites can be identified in typical clinical proteomic datasets acquired from the blood/serum samples of individual patient, from which the patient can be identified with high confidence. Our results suggested the presence of significant privacy risks in raw clinical proteomic data. However, these risks can be mitigated by a straightforward pre-processing step of the raw data that removing a very small fraction (0.1%, 7.14 out of 7,504 spectra on average) of MS/MS spectra identified as the minor allelic peptides, which has little or no impact on the subsequent analysis (and re-use) of these datasets.

Source: PubMed Central

Authors: Sujun Li, Nuno Bandeira, Xiaofeng Wang, Haixu Tang

Institutions: Indiana University Bloomington, Indiana University, University of California San Diego

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Human Genome Research Institute, National Institute of General Medical Sciences

Awards: Center for Computational Mass Spectrometry, Privacy preserving technologies for human genome data analysis and dissemination, Computational approaches to protein identification and quantification using MS/MS