9 until 1896, the law in the UK and elsewhere required a person

    10 until 1896, the law in the UK (and elsewhere) required a

    10 to walk in front of any moving car waving a red flag. Cars

    11 person to walk in front of any moving car waving a red flag.

    11 were such a novelty that most people did not know what to make

    12 Cars were such a novelty that most people did not know what to

    12 of them. The person with the red flag was intended to warn the

    13 make of them. The person with the red flag was intended to

    13 public, for example horse owners, about the impending

    14 warn the public, for example horse owners, about the impending

    21 ``privacy'' looks a little bit like the old Wild West.

    22 ``privacy'' looks a little bit like the old Wild

    23 West---lawless and mythical.

    26 money in order to be able to bombard you with spam. Yes, you

    28 money for bombarding you with spam. Yes, you can opt out very

    27 can opt out very often in such ``schemes'', but in case of

    29 often from such ``schemes'', but in case of UCAS any opt-out

    28 UCAS any opt-out will limit also legit emails you might

    30 will limit also legit emails you might actually be interested

    29 actually be interested in.\footnote{The main objectionable

    31 in.\footnote{The main objectionable point, in my opinion, is

    30 point, in my opinion, is that the \emph{charity} everybody has

    32 that the \emph{charity} everybody has to use for HE

    31 to use for HE applications has actually very honourable goals

    33 applications has actually very honourable goals (e.g.~assist

    32 (e.g.~assist applicants in gaining access to universities),

    34 applicants in gaining access to universities), but the small

    33 but the small print (or better the link ``About

    35 print (or better the link ``About us'') reveals they set up

    34 us'') reveals they set up their organisation so that they can

    36 their organisation so that they can also shamelessly sell the

    35 also shamelessly sell the email addresses they ``harvest''.

    37 email addresses they ``harvest''. Everything is of course very

    36 Everything is of course very legal\ldots{}moral?\ldots{}well

    38 legal\ldots{}moral?\ldots{}well that is in the eye of the

    37 that is in the eye of the beholder. See:

    39 beholder. See:

    56 \includegraphics[scale=0.19]{../pics/verizon.png}

    58 \includegraphics[scale=0.17]{../pics/verizon.png}

    57 \end{center}

    59 \end{center}

    58 

    60 

    59 \noindent How disgusting? Even worse, Verizon is not known for

    61 \noindent How disgusting! Even worse, Verizon is not known for

   146 statements that are often voiced in discussions about privacy:

   144 statements which are often voiced in discussions about privacy:

   149 \item \textit{``You have zero privacy anyway. Get over it.''}

   147 \item \textit{``You have zero privacy anyway. Get over 

   150 \mbox{}\hfill{}{\small{}by Scott Mcnealy (CEO of Sun)}

   148 it.''}\\

   149 \mbox{}\hfill{}{\small{}(by Scott Mcnealy, former CEO of Sun)}

   156 \noindent If you want to read up further on this topic, I can

   155 \noindent If you like to watch a movie which has this topic as

   157 recommend the following article that appeared in 2011 in the

   156 its main focus I recommend \emph{Gattaca} from

   158 Chronicle of Higher Education

   157 1997.\footnote{\url{http://www.imdb.com/title/tt0119177/}} If

   158 you want to read up on this topic, I can recommend the

   159 following article that appeared in 2011 in the Chronicle of

   160 Higher Education:

   284 deathbed, while she was actually very much alive and kicking.

   286 death bed, while she was actually very much alive and kicking.

   288 Differential privacy is one of the few methods, that tries to 

   290 Differential privacy is one of the few methods that tries to

   289 achieve forward privacy with large datasets. The basic idea

   291 achieve forward privacy. The basic idea is to add appropriate

   290 is to add appropriate noise, or errors, to any query of the

   292 noise, or errors, to any query of the dataset. The intention

   291 dataset. The intention is to make the result of a query 

   293 is to make the result of a query insensitive to individual

   292 insensitive to individual entries in the database. The hope is

   294 entries in the database. That means the results are

   293 that the added error does not eliminate the ``signal'' one is 

   295 approximately the same no matter if a particular individual is

   294 looking for by querying the dataset.

   296 in the dataset or not. The hope is that the added error does

   295 

   297 not eliminate the ``signal'' one is looking for in the

   296 

   298 dataset.

   297 

   299 

   298 \begin{center}

   300 %\begin{center}

   299 User\;\;\;\;    

   301 %User\;\;\;\;    

   300 \begin{tabular}{c}

   302 %\begin{tabular}{c}

   301 tell me $f(x)$ $\Rightarrow$\\

   303 %tell me $f(x)$ $\Rightarrow$\\

   302 $\Leftarrow$ $f(x) + \text{noise}$

   304 %$\Leftarrow$ $f(x) + \text{noise}$

   305 %\end{tabular}

   306 %\;\;\;\;\begin{tabular}{@{}c}

   307 %Database\\

   308 %$x_1, \ldots, x_n$

   309 %\end{tabular}

   310 %\end{center}

   311 %

   312 %\begin{center}

   313 %\begin{tabular}{l|l}

   314 %Staff & Salary\\\hline

   315 %$PM$ & \pounds{107}\\

   316 %$PF$ & \pounds{102}\\

   317 %$LM_1$ & \pounds{101}\\

   318 %$LF_2$ & \pounds{97}\\

   319 %$LM_3$ & \pounds{100}\\

   320 %$LM_4$ & \pounds{99}\\

   321 %$LF_5$ & \pounds{98}

   322 %\end{tabular}

   323 %\end{center}

   324 %

   325 %

   326 %\begin{center}

   327 %\begin{tikzpicture} 

   328 %\begin{axis}[symbolic y coords={salary},

   329 %             ytick=data,

   330 %             height=3cm]

   331 %\addplot+[jump mark mid] coordinates

   332 %{(0,salary)   (0.1,salary) 

   333 % (0.4,salary) (0.5,salary)  

   334 % (0.8,salary) (0.9,salary)};

   335 %\end{axis}

   336 %\end{tikzpicture}

   337 %\end{center}

   338 %

   339 %\begin{tikzpicture}[outline/.style={draw=#1,fill=#1!20}]

   340 %  \node [outline=red]            {red box};

   341 %  \node [outline=blue] at (0,-1) {blue box};

   342 %\end{tikzpicture}

   343 

   344 \ldots

   345 

   346 

   347 \subsubsection*{Further Reading}

   348 

   349 Two cool articles about how somebody obtained via the Freedom

   350 of Information Law the taxicab dataset of New York and someone

   351 else showed how easy it is to mine for private information: 

   352 

   353 \begin{center}

   354 \begin{tabular}{p{0.8\textwidth}}

   355 \url{http://chriswhong.com/open-data/foil_nyc_taxi/}\\

   356 \url{http://research.neustar.biz/2014/09/15/riding-with-the-stars-passenger-privacy-in-the-nyc-taxicab-dataset}

   304 \;\;\;\;\begin{tabular}{@{}c}

   358 \end{center}

   305 Database\\

   359 

   306 $x_1, \ldots, x_n$

   360 \noindent 

   307 \end{tabular}

   308 \end{center}

   309 

   310 \ldots

   311 

   312 

   313 \subsubsection*{Further Reading}

   314 

   316 habits (especially how they prey on young exhausted families

   362 habits (especially how they prey on new exhausted parents