Three tracks of competition tasks
Track 1: Practical Protection of Genomic Data Sharing through Beacon Services (privacy-preserving output release)
Given a sample Beacon database, we will ask participating team to develop solutions to mitigate the Bustamante attack. We will evaluate each algorithm based on the maximum number of correct queries that it can respond before any individual can be re-identified by the Bustamante attack. (data link)
Track 2: Privacy-Preserving Search of Similar Cancer Patients across Organizations (secure multiparty computing)
The scenario of this track is to find top-k most similar patients in a database on a panel of genes. The similarity is measured by the edit distance between a query sequence and sequences in the database. We expect participating teams come up with different algorithms that can provide good approximation to the actual edit distance and also be efficient. (data link)
Track 3: Testing for Genetic Diseases on Encrypted Genomes (secure outsourcing)
This is to calculate the probability of genetic diseases through matching a set of biomarkers to encrypted genomes that stored in a commercial cloud service. The requirement is that the entire matching process (only consider the exact match for each variation) needs to be carried out using homomorphic encryption so that no trace is left behind during the computation. (data link (example code updated on 5/22/2016) )
Given a sample Beacon database, we will ask participating team to develop solutions to mitigate the Bustamante attack. We will evaluate each algorithm based on the maximum number of correct queries that it can respond before any individual can be re-identified by the Bustamante attack. (data link)
Track 2: Privacy-Preserving Search of Similar Cancer Patients across Organizations (secure multiparty computing)
The scenario of this track is to find top-k most similar patients in a database on a panel of genes. The similarity is measured by the edit distance between a query sequence and sequences in the database. We expect participating teams come up with different algorithms that can provide good approximation to the actual edit distance and also be efficient. (data link)
Track 3: Testing for Genetic Diseases on Encrypted Genomes (secure outsourcing)
This is to calculate the probability of genetic diseases through matching a set of biomarkers to encrypted genomes that stored in a commercial cloud service. The requirement is that the entire matching process (only consider the exact match for each variation) needs to be carried out using homomorphic encryption so that no trace is left behind during the computation. (data link (example code updated on 5/22/2016) )