1. Jordan Brown (jbrown6@gatech.edu) & Douglas M. Bloughjbrown6@gatech.edu

2. It is difficult and time consuming to distribute different views of verifiable medical records. We want to make the process more manageable and efficient.

3. Data Provider Intermediary Data Consumers

4.  Application of the work seen in paper by Bauer, Blough, and Cash (ACM 2008)  Other similar approaches – (CDA Documents)  Wu et al (JMS 2010)  Slamanig and Stingl (IEEE 2009)  Slamanig and Rass (Springer 2010)

6. CONCEPTS FOR BUILDING MERKLE HASH TREES  Hash Function  One-way function  Variable length input  Maps to fixed length output  Statistically unlikely to find/calculate collisions  Computationally cheap compared to signatures  Public Key Signatures  Use secret key in combination with message to sign  Send signed message and original message  Using public key on signed message returns the original message  If actual message matches calculated message the signature verifies

7. Sign(Hash)Hash(1,2) Hash(1) 1 Hash(2) 2 Hash(3,4) Hash(3) 3 Hash(4) 4

8.  Redaction  Remove unused data  Keep Hashes  Prune Tree  Verification  Reconstruct remainder of tree  Verify the root signature Sign(Hash)Hash(1,2) Hash(1)Hash(2) Hash(3,4) Hash(3) 3 Hash(4) 4 21

9. … …… Root ………  Multi-level signatures  Comprehensive document across multiple sources

10. Sign(Hash)Hash(1,2) Hash(1)Hash(2) Hash(3,4) Hash(3)Hash(4)

13. SETUP  All times (CPU)  Eclipse 3.6.2 with Java SE 1.6  Windows 7 PC with 2.4 GHz Intel Core i5 and 4GB RAM DATASET  206 Records  Average element count of 190  Maximum element count was 828  Average extraction time was 312 ms  Optimizations have since been made (~10%)  Remaining results found with permutations of a single record

14.  Not included in time  Process single document  Extract relevant items  Included  Create leaves  Form tree  Sign root  Structure construction much more efficient than extracting elements Tree Construction

15.  Setup  Create multi-level tree with 20 sub-trees  Process  Randomly redact from even distribution of trees  Prune after each redaction  Very fast operation Tree Redaction

16.  Same process as previous redaction  Examining the remaining size of the tree

17.  Not included:  Document reconstruction  Included:  Reconstruct hashes  Verify root signature  Cost comparable with construction  Document reconstruction expensive Tree Verification

18.  Computationally Efficient Verifiable Redactable Data  Dependencies – Bauer et al. (ACM 2009)  Redaction Tracking – Izu et al. (2005)  Pseudonymization – Haber et al. (ACM 2008)  Sanitization (Invisibility) – Miyazaki et al. (ACM 2006)  Distributed Approach to Research Data Access Tracking and Control (joint work with Emory University Center for Clinical Informatics)