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\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 (cookie law, right-to-be-forgotten) 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.

For example, UCAS, a charity set up to help students to apply
to universities, has a commercial unit that happily sells your
email addresses to anybody who forks out enough money in order
to be able to bombard you with spam. Yes, you can opt out very
often in 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 in their
small print (or better under the link ``About us'') reveals
they set up their organisation so that they can also
shamelessly sell 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=0.19]{../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. 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 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.
A movie which has this topic as its main focus is Gattaca from
1997.\footnote{\url{http://www.imdb.com/title/tt0119177/}}


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

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

\item \textit{``If you have nothing to hide, you have nothing 
to fear.''}
\end{itemize}
 
\noindent If you want to read up further 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 very potentially private data, say health data. 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 of ratings. 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 including 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 these genes. In order
      to maintain privacy, the dataset would only include
      aggregate information. In case of DNA data this 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 agregate 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 be still 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''. Now 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 datasuch 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 data public in 
anonymised form you want to achieve \emph{forward privacy}.
This means, no matter of what other data that is also available
or will be released later, the data does not compromise
an individual's privacy. This principle was violated by the 
data in the Netflix and governor of Massachusetts cases. There
additional data allowed one to re-identify individuals in the
dataset. In case of GWAS a new technique of re-identification 
compromised the privacy of people on the list.
The case of the AOL dataset shows clearly how incomplete such 
data can be: Although the queries uniquely identified the
old 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 have concluded, the lady is on her deathbed, 
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 with large datasets. 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. The hope is
that the added error does not eliminate the ``signal'' one is 
looking for by querying 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}

\subsubsection*{Further Reading}

A readable article about how supermarkets mine your shopping
habits (especially how they prey on young exhausted families
;o) appeared in 2012 in a New York Times article.

\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 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
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