Title

Publishing Big Trajectory Data with Privacy Preservation

Presenter Information

Katrina Ward

Department

Computer Science

Major

Computer Science

Research Advisor

Lin, Dan

Advisor's Department

Computer Science

Funding Source

National Science Foundation (NSF)

Abstract

One of the biggest trends in mobile technology is the collection of trajectory data for analysis and location prediction. While the collection of such data, through mobile phones and vehicle GPS systems, is not new, current research searches for better ways to preserve the privacy of the users, whose data is being collected. Over the past few years, several methods have been introduced including k-anonymity, data suppression, and data masking, however, all of these methods fail to address the huge amount of data being generated by an entire city of users. The amount of data being transmitted every month is in the order of exabytes. In this paper, we propose a new method, using Map Reduce technology, of anonymizing huge data so that individual users cannot be identified in published data while also keeping as much of the data as possible. With Map Reduce being easy to manage on multiple commodity machines and easy to configure to dynamically choose the number of machines for a given task, we believe this method has more scalability and will continue to outperform traditional methods even as the amount of data becomes even larger.

Biography

Katrina is a senior in computer science and has been accepted into the Computer Science PhD program with full scholarship. She has done prior research with Dr. Jennifer Leopold in 3D Spatial and Temporal Reasoning. Currently, she works under her PhD advisor in Big Data and Privacy Preservation and has been awarded an NSF Undergraduate Fellowship for her work.

Research Category

Sciences

Presentation Type

Oral Presentation

Document Type

Presentation

Award

Sciences oral presentation, Second place

Location

Carver Room

Presentation Date

16 Apr 2014, 10:00 am - 10:30 am

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Apr 16th, 10:00 AM Apr 16th, 10:30 AM

Publishing Big Trajectory Data with Privacy Preservation

Carver Room

One of the biggest trends in mobile technology is the collection of trajectory data for analysis and location prediction. While the collection of such data, through mobile phones and vehicle GPS systems, is not new, current research searches for better ways to preserve the privacy of the users, whose data is being collected. Over the past few years, several methods have been introduced including k-anonymity, data suppression, and data masking, however, all of these methods fail to address the huge amount of data being generated by an entire city of users. The amount of data being transmitted every month is in the order of exabytes. In this paper, we propose a new method, using Map Reduce technology, of anonymizing huge data so that individual users cannot be identified in published data while also keeping as much of the data as possible. With Map Reduce being easy to manage on multiple commodity machines and easy to configure to dynamically choose the number of machines for a given task, we believe this method has more scalability and will continue to outperform traditional methods even as the amount of data becomes even larger.