Shade : a differentially private wrapper around Apache Spark
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119522 |
| _version_ | 1811085921178615808 |
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| author | Heifetz, Alexander G. (Alexander Garon) |
| author2 | Lalana Kagal. |
| author_facet | Lalana Kagal. Heifetz, Alexander G. (Alexander Garon) |
| author_sort | Heifetz, Alexander G. (Alexander Garon) |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. |
| first_indexed | 2024-09-23T13:17:32Z |
| format | Thesis |
| id | mit-1721.1/119522 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:17:32Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1195222019-04-11T10:41:11Z Shade : a differentially private wrapper around Apache Spark Heifetz, Alexander G. (Alexander Garon) Lalana Kagal. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. 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 85-88). Enterprises usually provide strong controls to prevent external cyberattacks and inadvertent leakage of data to external entities. However, in the case where employees and data scientists have legitimate access to analyze and derive insights from the data, there are insufficient controls and employees are usually permitted access to all information about the customers of the enterprise including sensitive and private information. Though it is important to be able to identify useful patterns of one's customers for better customization and service, customers' privacy must not be sacrificed to do so. We propose an alternative - a framework that will allow privacy preserving data analytics over big data. In this paper, we present an efficient and scalable framework for Apache Spark, a cluster computing framework, that provides strong privacy guarantees for users even in the presence of an informed adversary, while still providing high utility for analysts in an interactive wrapper. The framework, titled Shade, includes two mechanisms - SparkLAP, which provides Laplacian perturbation based on a user's query and SparkSAM, which uses the contents of the database itself in order to calculate the perturbation. We show that performance of Shade is substantially better than earlier differential privacy systems without loss of accuracy, particularly when run on datasets small enough to fit in memory, and find that SparkSAM can even exceed performance of an identical non-private Spark query. by Alexander G. Heifetz. M. Eng. 2018-12-11T20:38:39Z 2018-12-11T20:38:39Z 2017 2017 Thesis http://hdl.handle.net/1721.1/119522 1066694305 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 88 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Heifetz, Alexander G. (Alexander Garon) Shade : a differentially private wrapper around Apache Spark |
| title | Shade : a differentially private wrapper around Apache Spark |
| title_full | Shade : a differentially private wrapper around Apache Spark |
| title_fullStr | Shade : a differentially private wrapper around Apache Spark |
| title_full_unstemmed | Shade : a differentially private wrapper around Apache Spark |
| title_short | Shade : a differentially private wrapper around Apache Spark |
| title_sort | shade a differentially private wrapper around apache spark |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119522 |
| work_keys_str_mv | AT heifetzalexandergalexandergaron shadeadifferentiallyprivatewrapperaroundapachespark |