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Sep 14, 2020

Brief introduction to Differentially Private Machine Learning

In this post, I want to briefly introduce Differential Privacy to you, which, in my honest opinion, needs to get more attention in the software developer community. During my Master thesis, I evaluated the use of Differential Privacy for Federated Learning (I might explain Federated Learning in another post). The Theory Differential Privacy, originally $\epsilon$-Differential Privacy (DP)1, is a way to secure the privacy of individuals in a statistical database. A statistical database is a database, where only aggregation functions like “sum”, “average”, “count”, et cetera… can be executed. Read more

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  • Brief introduction to Differentially Private Machine Learning - Sep 14, 2020
  • 3D-GAN - Sep 4, 2020