Transcription of Chapter 8: Privacy Preserving Data Mining - uni …
{{id}} {{{paragraph}}}
Example: barber
Chapter 8: Privacy Preserving Data Mining - uni …
DATABASESYSTEMSGROUP1 Knowledge Discovery in DatabasesSS 2016 Lecture: Prof. Dr. Thomas SeidlTutorials: Julian Busch, Evgeniy Faerman,Florian Richter, Klaus SchmidLudwig-Maximilians-Universit t M nchenInstitut f r InformatikLehr-und Forschungseinheit f r DatenbanksystemeChapter 8: Privacy Preserving data MiningKnowledge Discovery in Databases I: Privacy Preserving data MiningDATABASESYSTEMSGROUP Introduction data Privacy Privacy Preserving data Mining k-Anonymity Privacy Paradigm k-Anonymity l-Diversity t-Closeness Differential Privacy Sensitivity, Noise Perturbation, CompositionPrivacy Preserving data Mining2 DATABASESYSTEMSGROUPHuge volume of data is collected from a variety of devices and platformsSuch as Smart Phones, Wearables,Social Networks, Medical systemsSuch data captures human behaviors,routines, activities and affiliationsWhile this overwhelming data collection provides an opportunity to perform data Privacy3 data AbuseData Abuse is inevitable.
DATABASE SYSTEMS GROUP A privacy paradigm for protecting database records before Data Publication Three kinds of attributes: – i) Key Attribute ii) Quasi-identifier ii) Sensitive Attribute
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: