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\renewcommand{\slidecaption}{APP 07, King's College London}
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\begin{document}
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\begin{frame}[t]
\frametitle{%
\begin{tabular}{@ {}c@ {}}
\\
\LARGE Access Control and \\[-3mm]
\LARGE Privacy Policies (7)\\[-6mm]
\end{tabular}}\bigskip\bigskip\bigskip
\normalsize
\begin{center}
\begin{tabular}{ll}
Email: & christian.urban at kcl.ac.uk\\
Office: & S1.27 (1st floor Strand Building)\\
Slides: & KEATS (also homework is there)\\
\end{tabular}
\end{center}
\end{frame}
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\begin{frame}[t]
\frametitle{Facebook Privacy}
\begin{itemize}
\item \large Who has a Facebook account?\pause\medskip
\item \large Who keeps the list of friends private?\pause\medskip
\item \large Who knows that this is completely pointless?
\textcolor{gray}{\small (at least at the end of 2013)}\pause\medskip
\end{itemize}
\only<4>{Create a fake account. Send a friend-request.
Facebook answers with ``People you may know'' feature.
Conveniently, it has also a ``see all'' button.}
\only<5>{\small\it ``Our policies explain that changing the
visibility of people on your friend list controls how they
appear on your Timeline, and that your friends may be visible
on other parts of the site, such as in News Feed, Search and
on other people's Timelines. This behavior is something we'll
continue to evaluate to make sure we're providing clarity.'' }
\end{frame}
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\begin{frame}
\frametitle{UCAS}
\mbox{}\\[-15mm]\mbox{}
\small
\begin{quote}
``The Universities and Colleges Admissions Service received more
than \pounds{12m} last year in return for sending targeted advertising
to subscribers as young as 16.
The service, which controls admissions to UK universities and
attracts 700,000 new applicants each year, sells the access
via its commercial arm, Ucas Media.
Vodafone, O2, Microsoft and the private university
accommodation provider Pure Student Living are among those who
have marketed through Ucas, which offers access to over a
million student email addresses\ldots
Applicants can opt out of receiving direct marketing, but only
at the cost of missing out on education and careers mailings
as well.''\bigskip\\
\footnotesize\hfill The Guardian, 12 March 2014
\end{quote}
\end{frame}
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\begin{frame}
\frametitle{Verizon}
\mbox{}\\[-23mm]\mbox{}
\begin{center}
\includegraphics[scale=0.21]{../pics/verizon.png}
\end{center}
\vfill\footnotesize
\url{http://webpolicy.org/2014/10/24/how-verizons-advertising-header-works}
\end{frame}
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\begin{frame}[c]
\frametitle{Privacy, Anonymity et al}
Some terminology:
\begin{itemize}
\item \alert{secrecy} is the mechanism used to limit the
number of principals with access to information (e.g.,
cryptography or access controls)
\item \alert{confidentiality} is the obligation to protect the
secrets of other people or organizations (secrecy for
the benefit of an organisation)
\item \alert{anonymity} is the ability to leave no evidence of
an activity (e.g., sharing a secret)
\item \alert{privacy} is the ability or right to protect your
personal secrets (secrecy for the benefit of an
individual)
\end{itemize}
\end{frame}
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\begin{frame}[t]
\frametitle{Privacy vs Anonymity}
\begin{itemize}
\item everybody agrees that anonymity has its uses (e.g.,
voting, whistleblowers, peer-review, exams)
\end{itemize}\bigskip\bigskip\pause
But privacy?\bigskip\bigskip
\textit{``You have zero privacy anyway. Get over it.''}\\
\hfill{}Scott Mcnealy (CEO of Sun)\bigskip\\
\textit{``If you have nothing to hide, you have nothing
to fear.''}
\end{frame}
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\begin{frame}[t]
\frametitle{Privacy Problems}
Private data can be often used against me:
\begin{itemize}
\item if my location data becomes public, thieves will switch
off their phones and help themselves in my home
\item if supermarkets can build a profile of what I buy, they
can use it to their advantage (banks - mortgages)
\item my employer might not like my opinions\bigskip\pause
\item one the other hand, Freedom-of-Information Act
\item medical data should be private, but medical research
needs data
\end{itemize}
\end{frame}
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\begin{frame}[t]
\frametitle{Privacy Problems}
\begin{itemize}
\item Apple takes note of every Siri dictation (sent over the
Internet to Apple; retained for 2 years)
\item markets often only work, if data is restricted (to build
trust)
\item social networks can reveal data about you
\item have you tried the collusion (lightbeam?) extension for
FireFox?
\item I do use Dropbox, store cards
\end{itemize}
\begin{textblock}{5}(12,9.9)
\includegraphics[scale=0.2]{../pics/gattaca.jpg}\\
\small Gattaca (1997)
\end{textblock}
\end{frame}
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\begin{frame}[t]
\frametitle{Privacy}
\begin{minipage}{1.05\textwidth}
\begin{itemize}
\item we \alert{do} want that government data is made public
(free maps for example)
\item we \alert{do not} want that medical data becomes public
(similarly tax data, school records, job search)\bigskip
\item personal information can potentially lead to fraud
(identity theft)
\end{itemize}\pause
{\bf ``The reality'':}
\only<2>{\begin{itemize}
\item London Health Programmes lost in 2011 unencrypted
details of more than 8 million people (no names, but
postcodes and details such as gender, age and ethnic
origin)
\end{itemize}}
\only<3>{\begin{itemize}
\item also in 2011, Sony got hacked: over 1M users' personal
information, including passwords, email addresses, home
addresses, dates of birth, and all Sony opt-in data
associated with their accounts.
\end{itemize}}
\only<4>{\begin{itemize}
\item in 2007, Gordon Brown needed to apologise for the loss
of tax data of 25M people (a junior civil servant sent
a CD in the mail, which got lost)
\end{itemize}}
\end{minipage}
\end{frame}
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\begin{frame}[c]
\frametitle{Privacy and Big Data}
\mbox{}\\[-16mm]\mbox{}
Selected sources of ``Big Data'':\smallskip{}
\begin{itemize}
\item Facebook
\begin{itemize}
\item 40+ Billion photos (100 PB)
\item 6 Billion messages daily (5 - 10 TB)
\item 900 Million users
\end{itemize}
\item Common Crawl
\begin{itemize}
\item covers 3.8 Billion webpages (2012 dataset)
\item 50 TB of data
\end{itemize}
\item Google
\begin{itemize}
\item 20 PB daily (2008)
\end{itemize}
\item Twitter
\begin{itemize}
\item 15 Million active users in the UK; 500M tweets per day
\item a company called Datasift is allowed to mine all tweets since 2010
\item they charge 10k per month for other companies to target advertisement
\end{itemize}
\end{itemize}\pause
\end{frame}
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\mode<presentation>{
\begin{frame}[c]
\frametitle{Cookies\ldots}
``We have published a new cookie policy. It explains what cookies are
and how we use them on our site. To learn more about cookies and
their benefits, please view our cookie policy.\medskip
If you'd like to disable cookies on this device, please view our information
pages on 'How to manage cookies'. Please be aware that parts of the
site will not function correctly if you disable cookies. \medskip
By closing this
message, you consent to our use of cookies on this device in accordance
with our cookie policy unless you have disabled them.''
\end{frame}}
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\mode<presentation>{
\begin{frame}[c]
\frametitle{Scare Tactics}
The actual policy reads:\bigskip
``As we explain in our Cookie Policy, cookies help you to get the most
out of our websites.\medskip
If you do disable our cookies you may find that certain sections of our
website do not work. For example, you may have difficulties logging in
or viewing articles.''
\end{frame}}
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\mode<presentation>{
\begin{frame}[c]
\frametitle{Netflix Prize}
Anonymity is \alert{necessary} for privacy, but \alert{not} enough!\bigskip
\begin{itemize}
\item Netflix offered in 2006 (and every year until 2010) a 1 Mio \$ prize for improving their movie rating algorithm
\item dataset contained 10\% of all Netflix users (appr.~500K)
\item names were removed, but included numerical ratings as well as times of rating
\item some information was \alert{perturbed} (i.e., slightly modified)
\end{itemize}
\hfill{\bf\alert{All OK?}}
\end{frame}}
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\mode<presentation>{
\begin{frame}[c]
\frametitle{Re-identification Attacks}
Two researchers analysed the data:
\begin{itemize}
\item with 8 ratings (2 of them can be wrong) and corresponding dates that can have a margin 14-day error, 98\% of the
records can be identified
\item for 68\% only two ratings and dates are sufficient (for movie ratings outside the top 500)\bigskip\pause
\item they took 50 samples from IMDb (where people can reveal their identity)
\item 2 of them uniquely identified entries in the Netflix database (either by movie rating or by dates)
\end{itemize}
\end{frame}}
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\mode<presentation>{
\begin{frame}[c]
\frametitle{Re-identification Attacks}
\begin{itemize}
\item in 1990 medical databases were made public with names removed, but birth dates,
gender, ZIP-code were retained\medskip
\item could be cross referenced with public voter registration data in order to find out what the
medical record of the governor of Massachusetts was (in 1997 Latanya Sweeney)
\end{itemize}
\end{frame}}
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\mode<presentation>{
\begin{frame}[c]
\frametitle{}
\begin{itemize}
\item Birth data, postcode and gender (unique for\\ 87\% of the US population)
\item Preferences in movies (99\% of 500K for 8 ratings)
\end{itemize}\bigskip
Therefore best practices / or even law (HIPAA, EU):
\begin{itemize}
\item only year dates (age group for 90 years or over),
\item no postcodes (sector data is OK, similarly in the US)\\
\textcolor{gray}{no names, addresses, account numbers, licence plates}
\item disclosure information needs to be retained for 5 years
\end{itemize}
\end{frame}}
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\begin{frame}[c]
\frametitle{AOL Search Queries}
\begin{itemize}
\item In 2006, AOL published 20 million Web search queries
collected of 650,000 users (names had been deleted)\medskip
\item \ldots{}within days an old lady, Thelma Arnold, from
Lilburn, Georgia, was identified as user No.~4417749\medskip
\item some of the queries that identified her away:
\begin{itemize}
\item landscapers in Lilburn, Ga
\item 60 single men
\item nicotine effects on the body
\item \ldots
\end{itemize}
\end{itemize}
\end{frame}
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\begin{frame}<2>[c]
\frametitle{\large How to Safely Disclose Information?}
\only<1>{
\begin{itemize}
\item Assume you make a survey of 100 randomly chosen people.
\item Say 99\% of the surveyed people in the 10 - 40 age group have seen the
Gangnam video on youtube.\bigskip
\item What can you infer about the rest of the population?
\end{itemize}}
\only<2>{
\begin{itemize}
\item Is it possible to re-identify data later, if more data is released? \bigskip\bigskip\pause
\item Not even releasing only aggregate information prevents re-identification attacks.
(GWAS was a public database of gene-frequency studies linked to diseases;
you only needed partial DNA information in order
to identify whether an individual was part of the study --- DB closed in 2008)
\end{itemize}}
\end{frame}
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\mode<presentation>{
\begin{frame}<2>[c]
\frametitle{\Large We cannot exclude all Harm}
\begin{itemize}
\item Analysis of a given data set teaches us that smoking causes cancer.
Mary, a smoker, is harmed by this analysis: her insurance premiums rise.
Mary’s premiums rise whether or not her data are in the data set. In other words,
Mary is harmed by the finding smoking causes cancer.\bigskip
\item \ldots of course she is also helped; she might quit smoking
\end{itemize}
\end{frame}}
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\mode<presentation>{
\begin{frame}<2>[c]
\frametitle{Differential Privacy}
\begin{itemize}
\item Goal: Nothing about an individual should be learnable from the database that
cannot be learned without access to the database.\pause\bigskip
\item Differential privacy is a ``protocol'' which you run on some dataset \bl{$X$} producing
some output \bl{$O(X)$}.\bigskip
\item You want to achieve \alert{forward privacy}
\end{itemize}
\end{frame}}
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\mode<presentation>{
\begin{frame}[c]
\frametitle{Differential Privacy}
\begin{center}
User\;\;\;\;
\begin{tabular}{c}
tell me \bl{$f(x)$} $\Rightarrow$\\
$\Leftarrow$ \bl{$f(x) + \text{noise}$}
\end{tabular}
\;\;\;\;\begin{tabular}{@{}c}
Database\\
\bl{$x_1, \ldots, x_n$}
\end{tabular}
\end{center}
\begin{itemize}
\item \bl{$f(x)$} can be released, if \bl{$f$} is insensitive to
individual entries \bl{$x_1, \ldots, x_n$}\\
\item Intuition: whatever is learned from the dataset would be learned regardless of whether
\bl{$x_i$} participates\bigskip\pause
\item Noise needed in order to prevent queries:\\ Christian's salary $=$
\begin{center}
\bl{\large$\Sigma$} all staff $-$ \bl{\large$\Sigma$} all staff $\backslash$ Christian
\end{center}
\end{itemize}
\end{frame}}
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\mode<presentation>{
\begin{frame}[c]
\frametitle{Example}
\begin{center}
\begin{tabular}{l|l}
Name & Has the disease?\\\hline
Alice & yes\\
Bob & no\\
Charlie & yes\\
Eve & no\\
Chandler & yes\\
\end{tabular}
\end{center}
How many people have a disease?
\end{frame}}
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\mode<presentation>{
\begin{frame}[c]
\frametitle{Adding Noise}
Adding noise is not as trivial as one would wish:
\begin{itemize}
\item If I ask how many of three have a disease and get a result
as follows
\begin{center}
\begin{tabular}{l|c}
Alice & yes\\
Bob & no\\
Charlie & yes\\
\end{tabular}
\end{center}
then I have to add a noise of \bl{$1$}. So answers would be in the
range of \bl{$1$} to \bl{$3$}
\bigskip
\item But if I ask five questions for all the dataset (has the disease, is male, below 30, \ldots),
then one individual can change the dataset by \bl{$5$}
\end{itemize}
\end{frame}}
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\mode<presentation>{
\begin{frame}[t]
\frametitle{\begin{tabular}{@{}c@{}}Tor (private web browsing)\end{tabular}}
\begin{itemize}
\item initially developed by US Navy Labs, but then opened up to the world
\item network of proxy nodes
\item a Tor client establishes a ``random'' path to the destination server (you cannot trace back where the information came from)\bigskip\pause
\end{itemize}
\only<2>{
\begin{itemize}
\item malicious exit node attack: someone set up 5 Tor exit nodes and monitored the traffic:
\begin{itemize}
\item a number of logons and passwords used by embassies (Usbekistan `s1e7u0l7c', while
Tunesia `Tunesia' and India `1234')
\end{itemize}
\end{itemize}}
\only<3>{
\begin{itemize}
\item bad apple attack: if you have one insecure application, your IP can be tracked through Tor
\begin{itemize}
\item background: 40\% of traffic on Tor is generated by BitTorrent
\end{itemize}
\end{itemize}}
\end{frame}}
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\mode<presentation>{
\begin{frame}[c]
\frametitle{Tor Nodes}
Dan Egerstad wrote:\bigskip
\it ``If you actually look in to where these Tor nodes are hosted and how big they are, some of these nodes cost thousands of dollars each month just to host because they're using lots of bandwidth, they're heavy-duty servers and so on. Who would pay for this and be anonymous?"
\end{frame}}
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\begin{frame}[t]
\frametitle{\begin{tabular}{@{}c@{}}Skype\end{tabular}}
\begin{itemize}
\item Skype used to be known as a secure online communication (encryption cannot be disabled),
but \ldots\medskip
\item it is impossible to verify whether crypto algorithms are correctly used, or whether there are backdoors.\bigskip
\item recently someone found out that you can reset the password of somebody else's
account, only knowing their email address (needed to suspended the password reset feature temporarily)
\end{itemize}
\end{frame}
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\begin{frame}[c]
\frametitle{Take Home Point}
According to Ross Anderson: \bigskip
\begin{itemize}
\item Creating large databases of sensitive personal information is intrinsically
hazardous (NHS)\bigskip
\item Privacy in a big hospital is just about doable.\medskip
\item How do you enforce privacy in something as big as Google
or complex as Facebook? Nobody knows.\bigskip
Similarly, big databases imposed by government.
\end{itemize}
\end{frame}
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\end{document}
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