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The Center for Education and Research in Information Assurance and Security (CERIAS)

The Center for Education and Research in
Information Assurance and Security (CERIAS)

Chenyun Dai - Purdue University

Students: Spring 2022, unless noted otherwise, sessions will be virtual on Zoom.

Privacy-Preserving Assessment of Location Data Trustworthiness

Mar 07, 2012

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Assessing the trustworthiness of location data corresponding to
individuals is essential in several applications, such as forensic
science and epidemic control. To obtain accurate and trustworthy
location data, analysts must often gather and correlate information
from several independent sources, e.g., physical observation, witness
testimony, surveillance footage, etc. However, such information may be
fraudulent, its accuracy may be low, and its volume may be insufficient
to ensure highly trustworthy data. On the other hand, recent
advancements in mobile computing and positioning systems, e.g.,
GPS-enabled cell phones, highway sensors, etc., bring new and
effective technological means to track the location of an individual.
Nevertheless, collection and sharing of such data must be done in ways
that do not violate an individual’s right to personal privacy.

Previous research efforts acknowledged the importance of assessing
location data trustworthiness, but they assume that data
is available to the analyst in direct, unperturbed form. However, such
an assumption is not realistic, due to the fact that repositories of
personal location data must conform to privacy regulations. In this
work, we study the challenging problem of refining trustworthiness of
location data with the help of large repositories of anonymized
information. We show how two important trustworthiness evaluation
techniques, namely common pattern analysis and conflict/support
analysis, can benefit from the use of anonymized location data. We have
implemented a prototype of the proposed privacy-preserving
trustworthiness evaluation techniques, and the experimental results
demonstrate that using anonymized data can significantly help in
improving the accuracy of location trustworthiness assessment.

About the Speaker

Chenyun Dai is currently a 5th year Ph.D. student in Computer Science
Department at Purdue University. He got his master degree in computer
science from Purdue University in 2010. Before coming to Purdue, he
got a master degree and a bachelor degree, both in computer science,
from Fudan University and East China Normal University respectively.
His Ph.D. dissertation addresses the development of a trustworthiness
models for information concerning locations of individuals. The
availability and correctness of this information is crucial for
important applications, namely forensics, criminal investigations, and
disease control and monitoring. A paper reporting the first results of
this research was accepted and presented at the 2009 ACM SIGSPATIAL
GIS Conference. More recently he has developed a major extension to
his model that supports the assessment of location data when location
data are only available in anonymized form and the work was publish in
2011 ACM SIGSPATIAL GIS Conference. He is currently extending this
work to support more sophisticated models for locations and
trajectories, uncertain data and social network data.

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