Differentially Private Publishing of Geospatial Data
Wahbeh Qardaji - Purdue University
Jan 23, 2013Size: 168.8MB
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AbstractWe interact with location-aware devices on a daily basis. Such devices range from GPS-enabled cell-phones and tablets, to navigation systems. Each device can report a multitude of location data to centralized servers. Such location information, commonly referred to as geospatial data, can have tremendous benefits if properly processed and analyzed. If shared, such geo-spatial data can have significant impact for research and other uses. Sharing such information, however, can have significant privacy implications. In this talk, we will focus on the problem of releasing static geo-spatial data in a private manner. In particular, we will explore methods of releasing a synopsis of two-dimensional datasets while satisfying differential privacy.
The key challenge to anonymizing geospatial datasets while satisfying differential privacy is ensuring the utility of anonymized dataset. In particular, there are two types of error that influence the utility of anonymized datasets. The first is the anonymization noise--a direct byproduct of the differential privacy mechanism. The second is a result of the granularity of data release and the nature of the dataset itself. In this talk, we will explore methods of publishing two-dimensional datasets with utility in mind. We will analyze the current state-of-the-art methods and explore alternative grid-based approaches that best balance the two sources of error.
About the SpeakerWahbeh Qardaji is a PhD candidate in the Computer Science department of Purdue University, and a member of Cerias. He received his Masters Degree in Computer Science in 2010 from Purdue University, and his Bachelors from the American University of Beirut. His research interests are in information security and data privacy. In particular, his research focuses on privacy preserving data publishing using differential privacy. His research advisor is Prof. Ninghui Li.
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