Exciting experience in participating EDM forum commissioned projects

1. Exciting experience in participating EDM forum commissioned projects Protect Patient Privacy When Sharing Data for CER 12/01/11 – 6/01/12 Write a commissioned paper (i.e., systematic review of privacy technologies of sharing data for Comparative Effectiveness Research) and present the paper at the June 15, 2012 EDM Forum Stakeholder Symposium. Making distributed models accessible and useful to data analysis 08/15/12 - 08/15/13 Test a practical user interface to Grid LOgisticREgression (GLORE) across clinical sites.

2. Motivation • We want to provide researchers a set of tools that enable efficient global data analyses without accessing patient-level health records. • The EDM forum commissioned project provides us an opportunity to access the viability of distributed model that builds model without sharing data.

3. Distributed data analysis

4. Distributed data analysis

5. Distributed data analysis

6. Distributed data analysis

7. Distributed data analysis

8. Distributed data analysis

9. Foundation of GLORE • Suppose m-1 features are consistent over k sites • In each iteration, intermediary results of a mxm matrix and a m-dimensional vector are transmitted to k-1 sites No exchanging of raw data Wu Y, Jiang X, Kim J, et al. Grid Binary LOgisticREgression (GLORE): building shared models without sharing data. J Am Med Inform Assoc 2012;2012:758–64.

10. Grid Logistic Regression as a webservice MIT license

11. Challenges and solutions • Privacy challenge JiZ, Jiang X, Wang S, et al. Differentially private distributed logistic regression using private and public data. BMC Med Genomics 2014;7:S14. • Efficiency challenge Wu Y, Jiang X, Kim J, et al. Grid Binary LOgisticREgression (GLORE): building shared models without sharing data. J Am Med Inform Assoc 2012;2012:758–64. • Institutional privacy concern Wu Y, Jiang X, Ohno-machado L. Preserving Institutional Privacy in Distributed Binary Logistic Regression. In: AMIA AnnuSymp. Chicago, IL: 2012. 1450–8. • UI challenge Jiang W, Li P, Wang S, et al. WebGLORE: a web service for Grid LOgisticREgression. Bioinformatics 2013;29:3238–40. • Implementation challenge Jiang W, Wang S, et al. Development of a web service for model building in a distributed network, eGEMs (under revision), 2014.

12. Beyond WebGLORE • How to collaborate more efficiently and securely? • Efficiency: Data user can delegate a part of collaborative studies into a cloud environment • Security: public cloud cannot learn any information about the underlying data protected by the cryptographic technologies

13. Beyond WebGLORE • How to collaborate more efficiently and securely? • Efficiency: Data user can delegate a part of collaborative studies into a cloud environment • Security: public cloud cannot learn any information about the underlying data protected by the cryptographic technologies

14. Beyond WebGLORE • How to collaborate more efficiently and securely? • Efficiency: Data user can delegate a part of collaborative studies into a cloud environment • Security: public cloud cannot learn any information about the underlying data protected by the cryptographic technologies

15. An Analogy: Alice’s necklace • Alice has some gemstones and gold

16. An Analogy: Alice’s necklace • Alice has some gemstones and gold • She wants to ask a worker to assemble raw materials into a necklace

17. An Analogy: Alice’s necklace • Alice has some gemstones and gold • She wants to ask a worker to assemble raw materials into a necklace But, Alice is worried about theft. She wants the worker to process the raw materials without having access to them

18. An Analogy: Alice’s necklace • Alice solves the problem by locking the materials in a glove box

19. An Analogy: Alice’s necklace • Alice solves the problem by locking the materials in a glove box • She asks the worker to assemble the necklace in the box

20. An Analogy: Alice’s necklace • Alice solves the problem by locking the materials in a glove box • She asks the worker to assemble the necklace in the box • She unlocks the box to get the necklace without worry about theft

21. The Analogy in Homomorphic (HM) Operations • HM Encryption: put things inside the locked box • Anyone can do this (e.g., a mail drop box) • Health data privacy can be protected by HM encryption

22. The Analogy in Homomorphic (HM) Operations • HM Encryption: put things inside the locked box • Anyone can do this (e.g., a mail drop box) • Health data privacy can be protected by HM encryption • HM Decryption: take the results out of the box • Only the person who has the key • Authorized researchers, stakeholders, etc.

23. The Analogy in Homomorphic (HM) Operations • HM Encryption: put things inside the locked box • Anyone can do this (e.g., a mail drop box) • Health data privacy can be protected by HM encryption • HM Decryption: take the results out of the box • Only the person who has the key • Authorized researchers, stakeholders, etc. • HM Evaluation: work on the materials • Anyone can do it. • Compute encrypted data in a cloud environment without sacrificing the privacy.

24. What can we do now using Homomorphic Encryption (HME)? Fully HME (e.g., enable unlimited number of both addition and multiplication on encrypted data) High Leveled HME (e.g., enable a certain number of both addition and multiplication on encrypted data) Flexibility Medium Partial HME (e.g., enable either addition or multiplication on encrypted data, but not both) Low High Medium Complexity

25. Challenges and solutions • Supporting more types of operations • HM encrypted data currently only support basic addition, multiplication or bit-wise shifting operations. • Approximate advanced operations with addition and multiplication operations. • E.g., Logarithm or exponential operation can be approximated by series expansion, which includes only addition and multiplication operations

26. Challenges and solutions • Supporting more types of operations • HM encrypted data currently only support basic addition, multiplication or bit-wise shifting operations. • Approximate advanced operations with addition and multiplication operations. • E.g., Logarithm or exponential operation can be approximated by series expansion, which includes only addition and multiplication operations • Supporting floating number • All the HM operations are taken place on integer • Use fixed point approximation • E.g., the floating number 0.5 can be represented by an integer of 128 with respect to a base 256 (0.5 = 128/256)

27. Challenges and solutions • Supporting more types of operations • HM encrypted data currently only support basic addition, multiplication or bit-wise shifting operations. • Approximate advanced operations with addition and multiplication operations. • E.g., Logarithm or exponential operation can be approximated by series expansion, which includes only addition and multiplication operations • Supporting floating number • All the HM operations are taken place on integer • Use fixed point approximation • E.g., the floating number 0.5 can be represented by an integer of 128 with respect to a base 256 (0.5 = 128/256) • Complexity issues • HM operations are computationally demanding • Use Parallel computing to speed up HM operations • Leverage partial HM, leveled HM and fully HM operations in different use cases.

28. Future work Homomorphic encrypted federated-cloud computing

29. Thank you! • What is next? • Two brief presentations to set the stage • Breakout sessions in the afternoon Room 304 “Analytical Methods for a Learning Healthcare System”Michael Stoto, Georgetown University “Distributed Statistical Model Fitting In Federated Networks: A user guide”Daniella Meeker, RAND Corporation and Jared Murray, Duke University Room 313