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Exciting experience in participating EDM forum commissioned projects. Protect Patient Privacy When Sharing Data for CER 12/01/11 – 6/01/12

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Exciting experience in participating edm forum commissioned projects
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.


Motivation
Motivation projects

  • 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.








Foundation of glore
Foundation of GLORE projects

  • 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.


Grid logistic regression as a webservice
Grid Logistic Regression projectsas a webservice

MIT license


Challenges and solutions
Challenges and solutions projects

  • 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.


Beyond webglore
Beyond WebGLORE projects

  • 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


Beyond webglore1
Beyond WebGLORE projects

  • 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


Beyond webglore2
Beyond WebGLORE projects

  • 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


An analogy alice s necklace
An Analogy: Alice’s necklace projects

  • Alice has some gemstones and gold


An analogy alice s necklace1
An Analogy: Alice’s necklace projects

  • Alice has some gemstones and gold

  • She wants to ask a worker to assemble raw materials into a necklace


An analogy alice s necklace2
An Analogy: Alice’s necklace projects

  • 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


An analogy alice s necklace3
An Analogy: Alice’s necklace projects

  • Alice solves the problem by locking the materials in a glove box


An analogy alice s necklace4
An Analogy: Alice’s necklace projects

  • Alice solves the problem by locking the materials in a glove box

  • She asks the worker to assemble the necklace in the box


An analogy alice s necklace5
An Analogy: Alice’s necklace projects

  • 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


The analogy in homomorphic hm operations
The Analogy in projectsHomomorphic (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


The analogy in homomorphic hm operations1
The Analogy in projectsHomomorphic (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.


The analogy in homomorphic hm operations2
The Analogy in projectsHomomorphic (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.


What can we do now using homomorphic encryption hme
What can we do now using projectsHomomorphic 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


Challenges and solutions1
Challenges and solutions projects

  • 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


Challenges and solutions2
Challenges and solutions projects

  • 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)


Challenges and solutions3
Challenges and solutions projects

  • 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.


Future work
Future work projects

Homomorphic encrypted federated-cloud computing


Thank you
Thank you! projects

  • 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


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