Protecting Privacy While Providing Utility in Published Network Mobility Traces Using Differential Privacy

Speaker : Jim Kurose
University of Massachusetts Amherst
Date: 29/01/2014
Time: 2:00 pm - 3:00 pm
Location: TPT

Abstract

Those who design, develop and deploy computer and networked systems, have a vital interest in how these systems perform in “real-world” scenarios. But real-world conditions and data sets are often hard to come by-companies treat scenario data as a confidential asset and public institutions are reluctant to release data for fear of compromising individual privacy. This challenge is particularly acute in mobile wireless networks, where many have noted the need for realistic mobility and wireless network datasets. But assuring privacy is difficult. Several well-known examples have shown how anonymized data sets can be combined with other data to compromise individuals’ personal privacy. The relatively recent model of differential privacy (DP) provides an alternate approach to measuring and controlling the disclosure of personal information, adding sufficient random “noise” (in a precisely quantifiable manner) to any output computed from a sensitive collection of data, so that a precise statistical privacy condition is met. In this talk we outline ongoing research to produce trajectory traces, and results derived from trajectory traces, for public release from original “real-world” mobility traces (e.g., from our 802.11 campus network) while providing both well-defined differential privacy guarantees and demonstrably high accuracy when these publicly-released data sets are used for a number of common network and protocol design and analysis tasks. We describe a DP technique using a constrained trajectory-prefix representationof the original data, using known network topology and human mobility constraints, to determinethe underlying representation of the original data and judiciously allocate random noise needed tosatisfy DP constraints. We will also discuss alternative representations of mobility data that we conjecture will provide better accuracy for specific analysis tasks, and discuss the tradeoff between generality/specificity and accuracy.This is a “work in-progress” talk, so ideas are still being “baked” and comments/discussion are particularly welcome. This is joint research with Gerome Miklau and Jennie Steshenko at the University of Massachusetts Amherst

Biography: Jim Kurose is a Distinguished Professor of Computer Science at the University of Massachusetts Amherst. His research interests include network protocols and architecture, network measurement, sensor networks, multimedia communication, and modeling and performance evaluation. He has served as Editor-in-Chief of the IEEE Transactions on Communications and was the founding Editor-in-Chief of the IEEE/ACM Transactions on Networking. He has been active in the program committees forIEEE Infocom, ACM SIGCOMM, ACM SIGMETRICS and ACM Internet Measurement conferences for a number of years, and has served as Technical Program Co-Chair for these conferences.He has received a number of research and teaching awards including the IEEE Infocom Award, the ACM Sigcomm Test of Time Award and the IEEE Taylor Booth Education Medal. With Keith Ross, he is the co-author of the textbook, Computer Networking, a top down approach (6th edition).He has been a visiting researcher at Technicolor’s Paris Research Lab and at the LINCS (where is also a member of the LINCS Scientific Advisory Board) in 2012.