1. Topics at the Interface of Privacy and Genomics
Anthony Philippakis, MD PhD Chief Data Officer, Broad Institute June 4th, 2018

2. Who am I? Disclaimer: I am not a crypto person!!!
Chief Data Officer of the Broad Institute
Lead the Data Sciences Platform
Previously studied pure math, but that’s ancient history
Trained as a cardiologist at BWH
Care for patients with rare, genetic CV diseases
Venture Partner at GV (Venture Capital)
Invest at intersection of tech and life sciences
Disclaimer: I am not a crypto person!!!

3. The Challenge of Scalability in Genomics
Globally, genomic data doubles every 8 months

4. Inverting the Model of Genomic Data Sharing
Traditional Approach: Bring data
to researchers
Opportunity: Bring researchers
to the data
Data
Public Cloud
Problems
Data sharing = data copying
Security (data handoffs)
Huge infrastructure needed
Siloed compute
Advantages
Cost
Threat detection and auditing
Increased Accessibility
Shared & elastic compute

5. Opportunities in Security & Compliance
Areas technology can have a big impact:
Management of data use
Multiparty Computation
Differential Privacy

6. Our Current Protocol for Data Access
Data Access Committee
Data Depositors
Data Use Limitations
Project Request Forms
Data Requestors
No!
This data is available for cancer research in a non-profit setting.
I am studying Breast cancer at a company.

7. Our Current Protocol for Data Access
Data Access Committee
Data Depositors
Data Use Limitations
Project Request Forms
Data Requestors
Yes!
This data is available for cancer research in a non-profit setting.
I am studying Breast cancer at a non-profit.

8. Human review of data access does not scale
Data Access Committee
Data Depositors
Data Use Limitations
Data Access Request
Data Requestors
Scales Poorly!! O(N2)
As of July 1, 2017
50,167 Submitted
34,16 Approved
Number of studies in dbGaP
5, Number of PIs requesting data
Number of PI countries
Number of publications resulting
from secondary data use
dbGaP at PRIM&R 2017

9. Problem: Data Use is not Coded!
Data Use Restrictions: What are you doing with the data?
“The donor wants her data used only for non-commercial cancer research”
Satisfies Data Use Restrictions
Yes No
Appropriate Permissions
Available
No access
Permissions: Who are you?
“Only consortium members can READ this data until it is published.”
Main Question: Can Data Use Restrictions be made machine-readable?

10. DUOS- Broad Data Use Oversight system
What is DUOS?
Interfaces to transform data use restrictions and data access requests to machine-readable code (ADA-M & Consent Codes)
A matching algorithm that checks if data access requests are compatible with data use restrictions
Interfaces for the Data Access Committee to adjudicate whether structuring and matching has been done appropriately

11. Validation of DUOS Claim: Data Use Can Test: Run a trial!
Be Structured
Test: Run a trial!
Data Access Committee
Pearl O’Rourke (Partners)
Laura Rodriguez (NIH)
John Wilbanks (Sage)
Stacey Donnelly (Broad)
Anthony Philippakis (Broad)
Diseases: Diabetes research only, Breast cancer research only, etc
Commercial Use: allowed/not allowed.
Special populations: Ethnicities, gender, pediatric, etc.
Future use for Methods Development, Aggregate Statistics, Controls
Review of ~150 Data Use Limitations Letters at Broad demonstrated that ~90% can be structured with the following ontologies
We have formed a DAC to compare automated review of access to traditional mode.

12. Initial results are very promising!
Validation of DUOS
Initial results are very promising!
>90% of data use restrictions were approved in structured form by the DAC

13. Opportunities in Security & Compliance
Areas technology can have a big impact:
Management of data use
Multiparty Computation
Differential Privacy

14. Multiparty Computation in Genomics
Areas where SMC could have a big impact in genomics:
The meta-analysis problem
Cohort 1
Cohort 2
Large cohorts have been assembled over many years as part of clinical trials and epidemiology research.
Many researchers (especially industry) are reluctant to share the whole cohort with another group.
However, it is often mutually advantageous to do a focused meta-analysis (e.g., enrichment of a variant)

15. Multiparty Computation in Genomics
Areas where SMC could have a big impact in genomics:
Geographic restrictions on data storage
Many countries are passing laws that data from human subjects needs to physically be stored in that country.
Clearly, we want to be able to cross-analyze large genetic cohorts from different countries.
Most would agree that it storing encrypted data is acceptable, however (i.e., secret-sharing paradigm).

16. Secure Data Exchange Secure Multiparty computation
Challenges to software-based implementations
Requires learning new, specialized programming languages
Increased computational overhead (a big deal, given size of genomic datasets)
With N parties, need to keep N copies of the data

17. Secure Data Exchange Secure Multiparty computation
Idea: Hardware-based approach
My group is in early days of exploring hardware-based SMC with Intel
Idea of building a “Data Switzerland” for life sciences

18. Opportunities in Security & Compliance
Areas technology can have a big impact:
Management of data use
Multiparty Computation
Differential Privacy

19. Differential Privacy
O(10^7)
O(10^1 – 10^4)
Individuals
O(10^6)
But growing fast
Genotypes
Phenotypes
Potential for Differential Privacy in human genetics
Huge interest in correlating genotypes and phenotypes to discover genetic basis of disease.
Real appetite for the idea of a trusted and trustworthy database that researchers can query against without risk of re-identifying participants

20. Differential Privacy Why hasn’t it happened??? (my $0.02)
O(10^7)
O(10^1 – 10^4)
Individuals
O(10^6)
But growing fast
Genotypes
Phenotypes
Why hasn’t it happened??? (my $0.02)
I’m told that allowing arbitrary computations requires adding a LOT of noise...
How much privacy is ok to leak? How do you doll out the privacy budget to researchers?
Is there a robust, production-grade system that is ready to use (and at this scale)?

21. Closing thoughts
The Broad Data Sciences Platform is a team of nearly 150 people that focus on making robust software products.
We are organized more like a tech company than an academic research group.
We are heavily involved in applied security efforts as part of large, national sequencing initiatives.
I would love for us to be more involved in innovation in things at intersection of life sciences and data sciences.
Please me if you think you might want to collaborate!