Case Studies in Differential Privacy for Computer
Networking Research
Name
meles-ameles-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
2.96 MB
Format
Adobe PDF
Checksum (MD5)
0a8fecb69f0eabc16796f834c7bd5639
Author(s)
Meles, Amelia
Advisor(s)
Clark, David D.
Chaganti, Vasanta
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright retained by author(s)
Persistent DSpace Link
