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dc.contributor.authorKing, Sam
dc.date.accessioned2019-01-31T18:58:19Z
dc.date.available2019-01-31T18:58:19Z
dc.date.issued2019-01-25
dc.identifier.urihttp://hdl.handle.net/1853/60856
dc.descriptionPresented on January 25, 2019 at 12:00 p.m. in the Krone Engineered Biosystems Building, Room 1005.en_US
dc.descriptionDr. Sam King was a professor for eight years at University Illinois Urbana-Champaign; however, five years ago he left his tenured position at UIUC to push himself intellectually and professionally in the industry. During these years, he started a company, sold his company to Twitter, worked as a code committing software engineer, fought fake accounts, managed a small team, managed a big team, secured Lyft’s phone-based accounts, battled fraudsters, and led a massive nine-month project (which is an eternity in industry) that ended up being the largest growth initiative in the history of Twitter. Now he's back in academia in the CS Department at UC Davis. He plans to continue to research topics in the computer security area and is especially interested in building systems for fighting fraud and rethinking our notion of digital identity. Dr. King received his Ph.D. from The University of Michigan, his Masters from Stanford, and his Bachelor’s degree from UCLA.en_US
dc.descriptionRuntime: 55:52 minutesen_US
dc.description.abstractLyft’s whole app experience is geared towards getting new users from the App Store or the Play Store to their first ride as quickly as possible. This streamlined process is great for users, but presents an ever-present problem — how do we prevent bad actors from abusing the lightweight onboarding process? In this talk, Dr. King will discuss how the best way to protect accounts is by changing products. He will describe the systems and techniques used at Twitter and Lyft to secure user's accounts, as well as extract general principles on how one should go about changing products to improve security. He will also discuss his research lab's recent work on preventing card-not-present - credit card fraud, where deep learning is used to create and detect fake credit cards.en_US
dc.format.extent55:52 minutes
dc.language.isoen_USen_US
dc.relation.ispartofseriesCybersecurity Lecture Seriesen_US
dc.subjectInformationen_US
dc.subjectProducten_US
dc.subjectSecurityen_US
dc.titleStopping Fraudsters by Changing Productsen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Information Security & Privacyen_US
dc.contributor.corporatenameUniversity of California, Davis. Dept. of Computer Scienceen_US


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