Data Privacy, Information Disclosure and Inference ControlSpeaker: RK Shyamasundar – Mumbai, India
Topic(s): Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science
AbstractWith historical and transactional data becoming available electronically, linking of data has led to privacy violations at an individual and organization levels as the linked data could reflect shadows of individuals or organizations. For a variety of reasons, information derived from confidential data must be declassified for wider distribution. For such purposes, the classic access control techniques are not sufficient. In this talk, we shall first discuss various attacks or inference techniques that can be used for extracting confidential or private information from general databases. Then, we shall discuss various approaches for anonymizing data that are widely used for preserving privacy of data such as k-anonymity, I-diversity and t-closeness, etc and discuss their limitations. Towards the end of the talk, we shall highlight the issues differential privacy is gearing to address.
About this LectureNumber of Slides: 75
Duration: 60 minutes
Languages Available: English
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