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Biology subjects

Wu, D. J.

Publications and source records attributed to Wu, D. J..

2 recordsLinked to original sources

Innocent until proven guilty: Privacy-preserving search over a central CODIS criminal database from the field

The presumption of innocence (i.e., the principle that one is considered innocent until proven guilty) is a cornerstone of the criminal justice system in many countries, including the United States. DNA analysis is an important tool for criminal investigations1. In the U.S. alone, it has already aided in over half a million investigations using the Combined DNA Index System (CODIS) and associated DNA databases2. CODIS includes DNA profiles of crime scene forensic samples, convicted offenders, missing persons and more. The CODIS framework is currently used by over 50 other countries3 including much of Europe, Canada, China and more. During investigations, DNA samples can be collected from multiple individuals who may have had access to, or were found near a crime scene, in the hope of finding a single criminal match4. Controversially, CODIS samples are sometimes retained from adults and juveniles despite not yielding any database match4-6. Here we introduce a cryptographic algorithm that finds any and all matches of a persons DNA profile against a CODIS database without revealing anything about the persons profile to the database provider. With our protocol, matches are immediately identified as before; however, individuals who do not match anything in the database retain their full privacy. Our novel algorithm runs in 40 seconds on a CODIS database of 1,000,000 entries, enabling its use to privately screen potentially-innocent suspects even in the field.

genomics↗

Revealing the causative variant in Mendelian patient genomes without revealing patient genomes

Given the rapidly growing utility of critical health information revealed in the human genome, secure genomic computation is essential to moving forward, especially as genome sequencing becomes commonplace. We devise and implement proof-of-principle computational operations for precisely identifying causal variants in Mendelian patients using secure multiparty computation methods based on Yaos protocol. We show multiple real scenarios (small patient cohorts, trio analysis, two hospital collaboration) where the causal variant is discovered jointly, while keeping up to 99.7% of all participants most sensitive genomic information private. All similar operations performed today to diagnose such cases are done openly, keeping 0% of participants genomic information private. Our work will help usher in an era where genomes can be both utilized and truly protected.

genomics↗