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Data analytics with differential privacy

Digalakis Vasileios

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URI: http://purl.tuc.gr/dl/dias/1E9F5FDC-3965-49D4-8CCD-18E7961AFF6F
Year 2018
Type of Item Diploma Work
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Bibliographic Citation Vassilis V. Digalakis Jr., "Data analytics with differential privacy", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2018 https://doi.org/10.26233/heallink.tuc.78371
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Summary

Differential privacy is the state-of-the-art definition for privacy, that “addresses the paradox of learning nothing about an individual while learning useful information about a population”. In other words, differential privacy guarantees that any analysis performed on a sensitive dataset leaks no information about the individuals whose data are contained therein. In this thesis, we develop differentially private algorithms to analyze distributed and streaming data.

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