slides/slides10.tex
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   454 \mode<presentation>{
   454 \mode<presentation>{
   455 \begin{frame}[c]
   455 \begin{frame}[c]
   456 \frametitle{\begin{tabular}{c}8th Lecture:\\ Privacy\end{tabular}}
   456 \frametitle{\begin{tabular}{c}8th Lecture: Privacy\end{tabular}}
   457 
   457 
   458 \begin{itemize}
   458 \begin{itemize}
   459 \item differential privacy for annonymizing research data
   459 \item differential privacy for annonymizing research data
       
   460 
       
   461 \begin{center}
       
   462 User\;\;\;\;    
       
   463 \begin{tabular}{c}
       
   464 tell me \bl{$f(x)$} $\Rightarrow$\\
       
   465 $\Leftarrow$ \bl{$f(x) + \text{noise}$}
       
   466 \end{tabular}
       
   467 \;\;\;\;\begin{tabular}{@{}c}
       
   468 Database\\
       
   469 \bl{$x_1, \ldots, x_n$}
       
   470 \end{tabular}
       
   471 \end{center}
       
   472 
       
   473 \item \bl{$f(x)$} can be released, if \bl{$f$} is insensitive to
       
   474 individual entries  \bl{$x_1, \ldots, x_n$}\\
       
   475 \item The intuition: whatever is learned from the dataset would be learned regardless of whether
       
   476 \bl{$x_i$} participates\bigskip\pause
       
   477 
   460 \item Tor webservice
   478 \item Tor webservice
   461 \end{itemize}
   479 \end{itemize}
   462 
   480 
   463 
   481 
   464 \end{frame}}
   482 \end{frame}}
   469 \begin{frame}[c]
   487 \begin{frame}[c]
   470 \frametitle{\begin{tabular}{c}9th Lecture:\\ Privacy\end{tabular}}
   488 \frametitle{\begin{tabular}{c}9th Lecture:\\ Privacy\end{tabular}}
   471 
   489 
   472 \begin{itemize}
   490 \begin{itemize}
   473 \item zero-knowledge proofs
   491 \item zero-knowledge proofs
   474 \item requires NP problems, for example graph isomorphisms
   492 \item requires NP problems, for example graph isomorphisms\bigskip\pause
       
   493 \item random number generators
   475 \end{itemize}
   494 \end{itemize}
   476 
   495 
   477 
   496 
   478 \end{frame}}
   497 \end{frame}}
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