Sharing Sensitive Department of Education Data Across Organizational Boundaries Using Secure Multiparty Computation

NCES Project Report

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Stephanie Straus, M.Ed., Georgetown University, Massive Data Institute

David Archer, Ph.D., Galois, Inc.

Amy O’Hara, Ph.D., Georgetown University, Massive Data Institute

Rawane Issa, M.S., Galois, Inc.

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Background

Push for inter-agency data sharing 

The Federal Data Strategy (FDS)

Foundations for Evidence-Based Policymaking Act of 2018 (H.R.4174) 

Hampered by privacy policy, statute, agency practices

Current privacy approaches carry risks

Solution → 

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     Privacy-Preserving Technologies 

                  Secure Multiparty Computation (SMC)

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Secure Multiparty Computation (SMC)

Subfield of cryptography

Joint computation of a calculation, among users with sensitive data

Performs computation while data remain encrypted 

Only aggregate results are decrypted 

Requires little to no hardware

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Current NPSAS Structure

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National Student Loan Data System

National Center for Education Statistics

National 

Post-Secondary Student Aid Study

Attendance & Sampling weights

Loan balances &

Grant amounts

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Potential NPSAS Structure with SMC

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National Center for Education Statistics

National Student Loan Data System

National 

Post-Secondary Student Aid Study

Encrypted Attendance & Sampling weights

Encrypted 

Loan balances &

Grant amounts

DecryptedResults

SMC

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NPSAS:16Table 6 - Excerpt

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How NPSAS Table 6 is Constructed Today - A Non Privacy Preserving Approach

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This Project: Privacy Preserving Computation Protocol

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NPSAS “Trust” Zone

NSLDS “Trust” Zone

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Demonstration Details

NCES Fellow Designation

Access to ED infrastructure

Uploaded 2 separate datasets, one per server

Simple command line interface

No “party” learns the other’s input

Aggregate results only revealed by SMC

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NPSAS:16Table 6 - Excerpt

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Sample Results, Showing Output, Runtime, Data Sizes

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|                                   | Unsubsidized Federal Direct Loans Awarded |                 |             |                |
|-----------------------------------|-------------------------------------------|-----------------|-------------|----------------|
|                                   | Ground Truth                              | Project Results |             |                |
|                                   | Avg. Loans ($)                            | Avg. Loans ($)  | Runtime (s) | Records Merged |
| Total                             | 4000                                      | 4100            | 278         | 34040          |
| Public: Less-than-2-year          | 4500                                      | 4600            | 138         | 100            |
| Public: 2-year                    | 3300                                      | 3300            | 137         | 1500           |
| Public: 4-year                    | 4000                                      | 4000            | 145         | 8070           |
| Non-doctorate-granting            | 3900                                      | 4000            | 145         | 2330           |
| Primarily subbaccalaureate        | 3600                                      | 3700            | 145         | 1050           |
| Primarily baccalaureate           | 4000                                      | 4000            | 139         | 1280           |
| Doctorate-granting                | 4000                                      | 4000            | 138         | 5740           |
| Private nonprofit: Less-than-4-yr | 4100                                      | 4200            | 139         | 520            |
| Private nonprofit: 4-year         | 3900                                      | 4000            | 137         | 5810           |
| Non-doctorate-granting            | 4000                                      | 4000            | 140         | 3090           |
| Doctorate-granting                | 3900                                      | 4000            | 144         | 2720           |

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Summary

Successful interagency data sharing

Privacy-preserving 

Accurate 

Efficient computation and network costs

Cost-effective  

Easy to use 

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Where To Go From Here?

First-of-its-kind demonstration 

Some challenges remain:

Legal issues

IT/Technical issues

Regulatory issues

Routine use

Future demonstration projects

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Thank You

stephanie.straus@georgetown.edu

See conference platform for technical paper.

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