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dc.contributor.authorShu, Xiaokuien_US
dc.contributor.authorYao, Danfeng (Daphne)en_US
dc.date.accessioned2013-05-28T20:43:58Zen_US
dc.date.accessioned2013-06-19T14:36:24Z
dc.date.available2013-05-28T20:43:58Zen_US
dc.date.available2013-06-19T14:36:24Z
dc.date.issued2012
dc.identifierhttp://eprints.cs.vt.edu/archive/00001194/en_US
dc.identifier.urihttp://hdl.handle.net/10919/19413
dc.descriptionWe describe a network-based data-leak detection (DLD) technique, the main feature of which is that the detection does not require the data owner to reveal the content of the sensitive data. Instead, only a small amount of specialized digests are needed. Our technique – referred to as the fuzzy fingerprint – can be used to detect accidental data leaks due to human errors or application flaws. The privacy-preserving feature of our algorithms minimizes the exposure of sensitive data and enables the data owner to safely delegate the detection to others.We describe how cloud providers can offer their customers data-leak detection as an add-on service with strong privacy guarantees. We perform extensive experimental evaluation on the privacy, efficiency, accuracy and noise tolerance of our techniques. Our evaluation results under various data-leak scenarios and setups show that our method can support accurate detection with very small number of false alarms, even when the presentation of the data has been transformed. It also indicates that the detection accuracy does not degrade when partial digests are used. We further provide a quantifiable method to measure the privacy guarantee offered by our fuzzy fingerprint framework.en_US
dc.formatpdf http://eprints.cs.vt.edu/archive/00001194/01/technicalreport.pdfen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.subjectParallel computationen_US
dc.titleData Leak Detection As a Service: Challenges and Solutionsen_US
dc.typeTechnical report - Departmentalen_US
dc.identifier.trnumberTR-12-10en_US
dc.type.dcmitypeTexten_US


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