Evidence map›Paper›PMID 29854245›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017

SCOTCH: Secure Counting Of encrypTed genomiC data using a Hybrid approach.

Wang Chenghong, Yichen Jiang, Noman Mohammed, Feng Chen, Xiaoqian Jiang, Md Momin Al Aziz, Md Nazmus Sadat, Shuang Wang

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Wang ChenghongDept. of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
Yichen JiangDept. of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
Noman MohammedDept. of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
Feng ChenDept. of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
Xiaoqian JiangDept. of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
Md Momin Al AzizDept. of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
Md Nazmus SadatDept. of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
Shuang WangDept. of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.

Funding

Decentralized differentially-private methods for dynamic data release and analysisR01GM118609 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN, OHNO-MACHADO, LUCILA · 2017 to 2020
$2.3M
Encryption methods and software for privacy-preserving analysis of biomedical dataU01EB023685 · NIBIB · TRUSTEES OF INDIANA UNIVERSITY · PI TANG, HAIXU, WANG, XIAOFENG · 2016 to 2018
$1.4M
Secure and PrivAte Collaborative EnvironmentS (SPACES) for biomedical analyticsR01GM118574 · NIGMS · RUTGERS THE STATE UNIV OF NJ NEWARK · PI VAIDYA, JAIDEEP · 2017 to 2019
$1.1M
SHARE: Statistical Health Information Release with Differential PrivacyR01GM114612 · NIGMS · EMORY UNIVERSITY · PI XIONG, LI · 2015 to 2017
$1.0M
Protecting the pRivacy Of Genomes in REsearch StudieS (PROGRESS)R00HG008175 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WANG, SHUANG · 2016 to 2018
$527k
Protect Privacy of Healthcare Data in the CloudR21LM012060 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN · 2014 to 2015
$376k
NHGRI NIH HHS R00 HG008175NIBIB NIH HHS U01 EB023685NIGMS NIH HHS R01 GM114612NIGMS NIH HHS R01 GM118574NIGMS NIH HHS R01 GM118609NLM NIH HHS R21 LM012060
6 · The paper itself

Abstract

As genomic data are usually at large scale and highly sensitive, it is essential to enable both efficient and secure analysis, by which the data owner can securely delegate both computation and storage on untrusted public cloud. Counting query of genotypes is a basic function for many downstream applications in biomedical research (e.g., computing allele frequency, calculating chi-squared statistics, etc.). Previous solutions show promise on secure counting of outsourced data but the efficiency is still a big limitation for real world applications. In this paper, we propose a novel hybrid solution to combine a rigorous theoretical model (homomorphic encryption) and the latest hardware-based infrastructure (i.e., Software Guard Extensions) to speed up the computation while preserving the privacy of both data owners and data users. Our results demonstrated efficiency by using the real data from the personal genome project.

Indexed as

Cloud ComputingComputer SecurityDatasets as TopicGenetic PrivacyGenomicsDatabases, GeneticGenome, HumanHumansModels, TheoreticalSoftware

Identifiers

PMID29854245
PMCPMC5977689

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.