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\fnote{\copyright{} Christian Urban, 2014}

\section*{Handout 7 (Privacy)}

The first motor car was invented around 1886. For ten years,
until 1896, the law in the UK (and elsewhere) required a
person to walk in front of any moving car waving a red flag.
Cars were such a novelty that most people did not know what to
make of them. The person with the red flag was intended to
warn the public, for example horse owners, about the impending
novelty---a car. In my humble opinion, we are at the same
stage of development with privacy. Nobody really knows what it
is about or what it is good for. All seems very hazy. There
are a few laws (e.g.~cookie law, right-to-be-forgotten law)
which address problems with privacy, but even if they are well
intentioned, they either back-fire or are already obsolete
because of newer technologies. The result is that the world of
``privacy'' looks a little bit like the old Wild
West---lawless and mythical.

For example, UCAS, a charity set up to help students with
applying to universities, has a commercial unit that happily
sells your email addresses to anybody who forks out enough
money for bombarding you with spam. Yes, you can opt out very
often from such ``schemes'', but in case of UCAS any opt-out
will limit also legit emails you might actually be interested
in.\footnote{The main objectionable point, in my opinion, is
that the \emph{charity} everybody has to use for HE
applications has actually very honourable goals (e.g.~assist
applicants in gaining access to universities), but the small
print (or better the link ``About us'') reveals they set up
their organisation so that they can also shamelessly sell the
email addresses they ``harvest''. Everything is of course very
legal\ldots{}moral?\ldots{}well that is in the eye of the
beholder. See:

\url{http://www.ucas.com/about-us/inside-ucas/advertising-opportunities} 
or
\url{http://www.theguardian.com/uk-news/2014/mar/12/ucas-sells-marketing-access-student-data-advertisers}}

Another example: Verizon, an ISP who is supposed to provide
you just with connectivity, has found a ``nice'' side-business
too: When you have enabled all privacy guards in your browser
(the few you have at your disposal), Verizon happily adds a
kind of cookie to your
HTTP-requests.\footnote{\url{http://webpolicy.org/2014/10/24/how-verizons-advertising-header-works/}}
As shown in the picture below, this cookie will be sent to
every web-site you visit. The web-sites then can forward the
cookie to advertisers who in turn pay Verizon to tell them
everything they want to know about the person who just made
this request, that is you.
 
\begin{center}
\includegraphics[scale=0.16]{../pics/verizon.png}
\end{center}

\noindent How disgusting! Even worse, Verizon is not known for
being the cheapest ISP on the planet (completely the
contrary), and also not known for providing the fastest
possible speeds, but rather for being among the few ISPs in
the US with a quasi-monopolistic ``market distribution''.


Well, we could go on and on\ldots{}and that has not even
started us yet with all the naughty things NSA \& Friends are
up to. Why does privacy actually matter? Nobody, I think, has
a conclusive answer to this question yet. Maybe the following
four notions help with clarifying the overall picture
somewhat: 

\begin{itemize}
\item \textbf{Secrecy} is the mechanism used to limit the
      number of principals with access to information (e.g.,
      cryptography or access controls). For example I better
      keep my password secret, otherwise people from the wrong
      side of the law might impersonate me.

\item \textbf{Confidentiality} is the obligation to protect
      the secrets of other people or organisations (secrecy
      for the benefit of an organisation). For example as a
      staff member at King's I have access to data, even
      private data, I am allowed to use in my work but not
      allowed to disclose to anyone else.

\item \textbf{Anonymity} is the ability to leave no evidence of
      an activity (e.g., sharing a secret). This is not equal
        with privacy---anonymity is required in many 
        circumstances, for example for whistle-blowers, 
        voting, exam marking and so on.

\item \textbf{Privacy} is the ability or right to protect your
      personal secrets (secrecy for the benefit of an
      individual). For example, in a job interview, I might
      not like to disclose that I am pregnant, if I were a
      woman, or that I am a father. Lest they might not hire
      me. Similarly, I might not like to disclose my location
      data, because thieves might break into my house if they
      know I am away at work. Privacy is essentially
      everything which ``shouldn't be anybody's business''.

\end{itemize}

\noindent While this might provide us with some rough
definitions, the problem with privacy is that it is an
extremely fine line what should stay private and what should
not. For example, since I am working in academia, I am every
so often very happy to be a digital exhibitionist: I am very
happy to disclose all `trivia' related to my work on my
personal web-page. This is a kind of bragging that is normal
in academia (at least in the field of CS), even expected if
you look for a job. I am even happy that Google maintains a
profile about all my academic papers and their citations. 

On the other hand I would be very irritated if anybody I do
not know had a too close look on my private live---it
shouldn't be anybody's business. The reason is that knowledge
about my private life usually is used against me. As mentioned
above, public location data might mean I get robbed. If
supermarkets build a profile of my shopping habits, they will
use it to \emph{their} advantage---surely not to \emph{my}
advantage. Also whatever might be collected about my life will
always be an incomplete, or even misleading, picture. For
example I am pretty sure my creditworthiness score was
temporarily(?) destroyed by not having a regular income in
this country (before coming to King's I worked in Munich for
five years). To correct such incomplete or flawed credit
history data there is, since recently, a law that allows you
to check what information is held about you for determining
your creditworthiness. But this concerns only a very small
part of the data that is held about me/you.

To see how private matter can lead really to the wrong
conclusions, take the example of Stephen Hawking: When he was
diagnosed with his disease, he was given a life expectancy of
two years. If employers would know about such problems, would
they have employed Hawking? Now, he is enjoying his 70+
birthday. Clearly personal medical data needs to stay private.

To cut a long story short, I let you ponder about the two
statements which are often voiced in discussions about privacy:

\begin{itemize}
\item \textit{``You have zero privacy anyway. Get over 
it.''}\\
\mbox{}\hfill{}{\small{}(by Scott Mcnealy, former CEO of Sun)}

\item \textit{``If you have nothing to hide, you have nothing 
to fear.''}
\end{itemize}
 
\noindent If you like to watch a movie which has this topic as
its main focus I recommend \emph{Gattaca} from
1997.\footnote{\url{http://www.imdb.com/title/tt0119177/}} If
you want to read up on this topic, I can recommend the
following article that appeared in 2011 in the Chronicle of
Higher Education:

\begin{center} 
\url{http://chronicle.com/article/Why-Privacy-Matters-Even-if/127461/} 
\end{center} 

\noindent Funnily, or maybe not so funnily, the author of this
article carefully tries to construct an argument that does not
only attack the nothing-to-hide statement in cases where
governments \& co collect people's deepest secrets, or
pictures of people's naked bodies, but an argument that
applies also in cases where governments ``only'' collect data
relevant to, say, preventing terrorism. The fun is of course
that in 2011 we could just not imagine that respected
governments would do such infantile things as intercepting
people's nude photos. Well, since Snowden we know some people
at the NSA did exactly that and then shared such photos among
colleagues as ``fringe benefit''.  


\subsubsection*{Re-Identification Attacks} 

Apart from philosophical musings, there are fortunately also
some real technical problems with privacy. The problem I want
to focus on in this handout is how to safely disclose datasets
containing potentially very private data, say health records.
What can go wrong with such disclosures can be illustrated
with four well-known examples:

\begin{itemize}
\item In 2006, a then young company called Netflix offered a 1
      Mio \$ prize to anybody who could improve their movie
      rating algorithm. For this they disclosed a dataset
      containing 10\% of all Netflix users at the time
      (appr.~500K). They removed names, but included numerical
      ratings of movies as well as times when ratings were
      uploaded. Though some information was perturbed (i.e.,
      slightly modified).
      
      Two researchers had a closer look at this anonymised
      data and compared it with public data available from the
      International Movie Database (IMDb). They found that
      98\% of the entries could be re-identified in the
      Netflix dataset: either by their ratings or by the dates
      the ratings were uploaded. The result was a class-action
      suit against Netflix, which was only recently resolved
      involving a lot of money.

\item In the 1990ies, medical datasets were often made public
      for research purposes. This was done in anonymised form
      with names removed, but birth dates, gender and ZIP-code
      were retained. In one case where such data about
      hospital visits of state employees in Massachusetts was
      made public, the then governor assured the public that
      the released dataset protected patient privacy by
      deleting identifiers. 
      
      A graduate student could not resist cross-referencing
      public voter data with the released data that still
      included birth dates, gender and ZIP-code. The result
      was that she could send the governor his own hospital
      record. It turns out that birth dates, gender and
      ZIP-code uniquely identify 87\% of people in the US.
      This work resulted in a number of laws prescribing which
      private data cannot be released in such datasets.
 
\item In 2006, AOL published 20 million Web search queries
      collected from 650,000 users (names had been deleted).
      This was again done for research purposes. However,
      within days an old lady, Thelma Arnold, from Lilburn,
      Georgia, (11,596 inhabitants) was identified as user
      No.~4417749 in this dataset. It turned out that search
      engine queries are deep windows into people's private
      lives. 
  
\item Genome-Wide Association Studies (GWAS) was a public
      database of gene-frequency studies linked to diseases.
      It would essentially record that people who have a
      disease, say diabetes, have also certain genes. In order
      to maintain privacy, the dataset would only include
      aggregate information. In case of DNA data this
      aggregation was achieved by mixing the DNA of many
      individuals (having a disease) into a single solution.
      Then this mixture was sequenced and included in the
      dataset. The idea was that the aggregate information
      would still be helpful to researchers, but would protect
      the DNA data of individuals. 
       
      In 2007 a forensic computer scientist showed that
      individuals can still be identified. For this he used
      the DNA data from a comparison group (people from the
      general public) and ``subtracted'' this data from the
      published data. He was left with data that included all
      ``special'' DNA-markers of the individuals present in
      the original mixture. He essentially deleted the
      ``background noise'' in the published data. The
      problem with DNA data is that it is of such a high
      resolution that even if the mixture contained maybe 100
      individuals, you can now detect whether an individual
      was included in the mixture or not.
      
      This result changed completely how DNA data is nowadays
      published for research purposes. After the success of 
      the human-genome project with a very open culture of
      exchanging data, it became much more difficult to 
      anonymise data so that patient's privacy is preserved.
      The public GWAS database was taken offline in 2008.
      
\end{itemize}

\noindent There are many lessons that can be learned from
these examples. One is that when making datasets public in
anonymised form, you want to achieve \emph{forward privacy}.
This means, no matter what other data that is also available
or will be released later, the data in the original dataset
does not compromise an individual's privacy. This principle
was violated by the availability of ``outside data'' in the
Netflix and governor of Massachusetts cases. The additional
data permitted a re-identification of individuals in the
dataset. In case of GWAS a new technique of re-identification
compromised the privacy of people in the dataset. The case of
the AOL dataset shows clearly how incomplete such data can be:
Although the queries uniquely identified the older lady, she
also looked up diseases that her friends had, which had
nothing to do with her. Any rational analysis of her query
data must therefore have concluded, the lady is on her
death bed, while she was actually very much alive and kicking.

\subsubsection*{Differential Privacy}

Differential privacy is one of the few methods that tries to
achieve forward privacy. The basic idea is to add appropriate
noise, or errors, to any query of the dataset. The intention
is to make the result of a query insensitive to individual
entries in the database. That means the results are
approximately the same no matter if a particular individual is
in the dataset or not. The hope is that the added error does
not eliminate the ``signal'' one is looking for in the
dataset.

%\begin{center}
%User\;\;\;\;    
%\begin{tabular}{c}
%tell me $f(x)$ $\Rightarrow$\\
%$\Leftarrow$ $f(x) + \text{noise}$
%\end{tabular}
%\;\;\;\;\begin{tabular}{@{}c}
%Database\\
%$x_1, \ldots, x_n$
%\end{tabular}
%\end{center}
%
%\begin{center}
%\begin{tabular}{l|l}
%Staff & Salary\\\hline
%$PM$ & \pounds{107}\\
%$PF$ & \pounds{102}\\
%$LM_1$ & \pounds{101}\\
%$LF_2$ & \pounds{97}\\
%$LM_3$ & \pounds{100}\\
%$LM_4$ & \pounds{99}\\
%$LF_5$ & \pounds{98}
%\end{tabular}
%\end{center}
%
%
%\begin{center}
%\begin{tikzpicture} 
%\begin{axis}[symbolic y coords={salary},
%             ytick=data,
%             height=3cm]
%\addplot+[jump mark mid] coordinates
%{(0,salary)   (0.1,salary) 
% (0.4,salary) (0.5,salary)  
% (0.8,salary) (0.9,salary)};
%\end{axis}
%\end{tikzpicture}
%\end{center}
%
%\begin{tikzpicture}[outline/.style={draw=#1,fill=#1!20}]
%  \node [outline=red]            {red box};
%  \node [outline=blue] at (0,-1) {blue box};
%\end{tikzpicture}

\ldots


\subsubsection*{Further Reading}

Two cool articles about how somebody obtained via the Freedom
of Information Law the taxicab dataset of New York and someone
else showed how easy it is to mine for private information: 

\begin{center}
\begin{tabular}{p{0.8\textwidth}}
\url{http://chriswhong.com/open-data/foil_nyc_taxi/}\\
\url{http://research.neustar.biz/2014/09/15/riding-with-the-stars-passenger-privacy-in-the-nyc-taxicab-dataset}
\end{tabular}
\end{center}

\noindent 
A readable article about how supermarkets mine your shopping
habits (especially how they prey on new exhausted parents
;o) appeared in 2012 in the New York Times:

\begin{center}
\url{http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html}
\end{center}

\noindent An article that analyses privacy and shopping habits 
from a more economic point is available from:

\begin{center}
\url{http://www.dtc.umn.edu/~odlyzko/doc/privacy.economics.pdf}
\end{center}

\noindent An attempt to untangle the web of current technology
for spying on consumers is published in:

\begin{center}
\url{http://cyberlaw.stanford.edu/files/publication/files/trackingsurvey12.pdf}
\end{center}

\noindent An article that sheds light on the paradox that
people usually worry about privacy invasions of little
significance, and overlook the privacy invasion that might
cause significant damage:

\begin{center}
\url{http://www.heinz.cmu.edu/~acquisti/papers/Acquisti-Grossklags-Chapter-Etrics.pdf}
\end{center}

\end{document}

http://randomwalker.info/teaching/fall-2012-privacy-technologies/?
http://chronicle.com/article/Why-Privacy-Matters-Even-if/127461/
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1077&context=hcii
https://josephhall.org/papers/NYU-MCC-1303-S2012_privacy_syllabus.pdf
http://www.jetlaw.org/wp-content/uploads/2014/06/Bambauer_Final.pdf
http://www.cs.cmu.edu/~yuxiangw/docs/Differential%20Privacy.pdf
https://www.youtube.com/watch?v=Gx13lgEudtU
https://www.cs.purdue.edu/homes/ctask/pdfs/CERIAS_Presentation.pdf
http://www.futureofprivacy.org/wp-content/uploads/Differential-Privacy-as-a-Response-to-the-Reidentification-Threat-Klinefelter-and-Chin.pdf
http://www.cis.upenn.edu/~aaroth/courses/slides/Overview.pdf
http://www.cl.cam.ac.uk/~sjm217/papers/tor14design.pdf

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