Chenyun Dai's guest appearances

Chenyun Dai

CERIAS Weekly Security Seminar - Purdue University

Ryan Henry

Cory Doctorow

Doug Smith

Larry Ponemon

George Heron

Laura Thomas

Eli Lilly

Stephen Elliott

Tony Sager

David Carroll

Jack Daniel

Joe Judge

Chris Reed

Richard III

John Richardson

OwlTail

Assessing the trustworthiness of location data corresponding toindividuals is essential in several applications, such as forensicscience and epidemic control. To obtain accurate and trustworthylocation data, analysts must often gather and correlate informationfrom several independent sources, e.g., physical observation, witnesstestimony, surveillance footage, etc. However, such information may befraudulent, its accuracy may be low, and its volume may be insufﬁcientto ensure highly trustworthy data. On the other hand, recentadvancements in mobile computing and positioning systems, e.g.,GPS-enabled cell phones, highway sensors, etc., bring new andeffective technological means to track the location of an individual.Nevertheless, collection and sharing of such data must be done in waysthat do not violate an individual's right to personal privacy.Previous research efforts acknowledged the importance of assessinglocation data trustworthiness, but they assume that datais available to the analyst in direct, unperturbed form. However, suchan assumption is not realistic, due to the fact that repositories ofpersonal location data must conform to privacy regulations. In thiswork, we study the challenging problem of reﬁning trustworthiness oflocation data with the help of large repositories of anonymizedinformation. We show how two important trustworthiness evaluationtechniques, namely common pattern analysis and conﬂict/supportanalysis, can beneﬁt from the use of anonymized location data. We haveimplemented a prototype of the proposed privacy-preservingtrustworthiness evaluation techniques, and the experimental resultsdemonstrate that using anonymized data can signiﬁcantly help inimproving the accuracy of location trustworthiness assessment. About the speaker: Chenyun Dai is currently a 5th year Ph.D. student in Computer ScienceDepartment at Purdue University. He got his master degree in computerscience from Purdue University in 2010. Before coming to Purdue, hegot 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 trustworthinessmodels for information concerning locations of individuals. Theavailability and correctness of this information is crucial forimportant applications, namely forensics, criminal investigations, anddisease control and monitoring. A paper reporting the first results ofthis research was accepted and presented at the 2009 ACM SIGSPATIALGIS Conference. More recently he has developed a major extension tohis model that supports the assessment of location data when locationdata are only available in anonymized form and the work was publish in2011 ACM SIGSPATIAL GIS Conference. He is currently extending thiswork to support more sophisticated models for locations andtrajectories, uncertain data and social network data.

<p>Assessing the trustworthiness of location data corresponding toindividuals is essential in several applications, such as forensicscience and epidemic control. To obtain accurate and trustworthylocation data, analysts must often gather and correlate informationfrom several independent sources, e.g., physical observation, witnesstestimony, surveillance footage, etc. However, such information may befraudulent, its accuracy may be low, and its volume may be insufﬁcientto ensure highly trustworthy data. On the other hand, recentadvancements in mobile computing and positioning systems, e.g.,GPS-enabled cell phones, highway sensors, etc., bring new andeffective technological means to track the location of an individual.Nevertheless, collection and sharing of such data must be done in waysthat do not violate an individual's right to personal privacy.Previous research efforts acknowledged the importance of assessinglocation data trustworthiness, but they assume that datais available to the analyst in direct, unperturbed form. However, suchan assumption is not realistic, due to the fact that repositories ofpersonal location data must conform to privacy regulations. In thiswork, we study the challenging problem of reﬁning trustworthiness oflocation data with the help of large repositories of anonymizedinformation. We show how two important trustworthiness evaluationtechniques, namely common pattern analysis and conﬂict/supportanalysis, can beneﬁt from the use of anonymized location data. We haveimplemented a prototype of the proposed privacy-preservingtrustworthiness evaluation techniques, and the experimental resultsdemonstrate that using anonymized data can signiﬁcantly help inimproving the accuracy of location trustworthiness assessment. About the speaker: Chenyun Dai is currently a 5th year Ph.D. student in Computer ScienceDepartment at Purdue University. He got his master degree in computerscience from Purdue University in 2010. Before coming to Purdue, hegot 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 trustworthinessmodels for information concerning locations of individuals. Theavailability and correctness of this information is crucial forimportant applications, namely forensics, criminal investigations, anddisease control and monitoring. A paper reporting the first results ofthis research was accepted and presented at the 2009 ACM SIGSPATIALGIS Conference. More recently he has developed a major extension tohis model that supports the assessment of location data when locationdata are only available in anonymized form and the work was publish in2011 ACM SIGSPATIAL GIS Conference. He is currently extending thiswork to support more sophisticated models for locations andtrajectories, uncertain data and social network data.</p>

Chenyun Dai, Privacy-Preserving Assessment of Location Data Trustworthiness