2026-09-19T11:40:32Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1514542023-08-01T03:09:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Clark, David D.
Chaganti, Vasanta
Meles, Amelia
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:41:00Z
2023-07-31T19:41:00Z
2023-06
2023-06-06T16:35:43.859Z
https://hdl.handle.net/1721.1/151454
We conduct two case studies on the use of differential privacy in computer networking research: private analysis of 1) Internet performance measurements from the Measuring Broadband America dataset and 2) flow-based network traces from the NF-UNSW-NB15 Netflow dataset. We survey two open-source tools for this analysis, Ektelo and Tumult Analytics, and evaluate the experience for a data practitioner at each step of designing a differentially private statistical release with each of these tools. In Ektelo, we asses the privacy versus utility trade-off for 5 algorithms (Identity, H2, HB, GreedyH, and DAWA) and provide examples of context-specific utility functions and post-processing techniques for the Internet measurement data.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Case Studies in Differential Privacy for Computer
Networking Research
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="d6f78df5-d6b9-407e-9ade-2a6e429000e9">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Case Studies in Differential Privacy for Computer
Networking Research</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2023-06</PublicationDate>
<Authors>
<Author>
<DisplayName>Meles, Amelia</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>https://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>We conduct two case studies on the use of differential privacy in computer networking research: private analysis of 1) Internet performance measurements from the Measuring Broadband America dataset and 2) flow-based network traces from the NF-UNSW-NB15 Netflow dataset. We survey two open-source tools for this analysis, Ektelo and Tumult Analytics, and evaluate the experience for a data practitioner at each step of designing a differentially private statistical release with each of these tools. In Ektelo, we asses the privacy versus utility trade-off for 5 algorithms (Identity, H2, HB, GreedyH, and DAWA) and provide examples of context-specific utility functions and post-processing techniques for the Internet measurement data.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>