Natural language processing on encrypted patient data
Name
1020068666-MIT.pdf
Description
Full printable version
Size
2.52 MB
Format
Adobe PDF
Checksum (MD5)
c6df5ce846ab1ac5c4369e8afbb72c6f
Author(s)
Grinman, Alex J
Advisor(s)
Shafi Goldwasser.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
While many industries can benefit from machine learning techniques for data analysis, they often do not have the technical expertise nor computational power to do so. Therefore, many organizations would benefit from outsourcing their data analysis. Yet, stringent data privacy policies prevent outsourcing sensitive data and may stop the delegation of data analysis in its tracks. In this thesis, we put forth a two-party system where one party capable of powerful computation can run certain machine learning algorithms from the natural language processing domain on the second party's data, where the first party is limited to learning only specific functions of the second party's data and nothing else. Our system provides simple cryptographic schemes for locating keywords, matching approximate regular expressions, and computing frequency analysis on encrypted data. We present a full implementation of this system in the form of a extendible software library and a command line interface. Finally, we discuss a medical case study where we used our system to run a suite of unmodified machine learning algorithms on encrypted free text patient notes.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-86).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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