Graph Identification and Privacy in Social Networks
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Uploaded by GoogleTechTalks on Dec 17, 2008
Google Tech Talks
December 16, 2008
ABSTRACT
Graph identification refers to methods that transform observational data described as a noisy, input graph into an inferred "clean" output graph. Examples include inferring social networks from communication data, identifying gene regulatory networks from protein-protein interactions, etc. On the flip-side, there is a growing interest in anonymizing social network data, and understanding the different types of privacy threats inherent in relational data. In this talk, I will discuss some of the key processes involved in identification (entity resolution, link prediction, collective classification and group detection) and I will overview results showing that on several well-known social media sites, we can easily and accurately recover information that users may wish to remain private.
Speaker: Lise Getoor
Lise Getoor is an associate professor in the Computer Science Department at the University of Maryland, College Park. She received her PhD from Stanford University in 2001. Her current work includes research on link mining, statistical relational learning and representing uncertainty in structured and semi-structured data. She has published numerous articles in machine learning, data mining, database, and artificial intelligence forums. She was awarded an NSF Career Award, is an action editor for the Machine Learning Journal, a JAIR associate editor, has been a member of AAAI Executive council, and has served on a variety of program committees including AAAI, ICML, IJCAI, KDD, SIGMOD, UAI, VLDB, and WWW. More information can be found at www.cs.umd.edu/~getoor
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useful information
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bass109 11 months ago
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matinmadona 1 year ago
Nice work. keep it up. fghyfuh
EdgardoJustice 2 years ago
This comment has received too many negative votes show
Awesome I will use this research to help stalk my ex-girlfriend THAT FUCKING BITCH
fjkgm5 3 years ago
Good talk from Lise Getoor.
amxambe 3 years ago