these days companies are using more and more of our data to improve their
products and services and it makes a lot of sense if you think about it it's
better to measure what your users like than to guess and build products that no
one wants to use however this is also very dangerous
it undermines our privacy because the collected data can be quite sensitive
causing harm if it would leak so companies love data to improve their
products but we as users we want to protect our privacy
these contradicting needs can be satisfied with a technique called
differential privacy it allows companies to collect information about their users
without compromising the privacy of an individual but let's first take a look
at why we would go through all this trouble companies can just take our data
remove our names and call it a day right well not quite first of all this
anonymization process usually happens on the servers of the companies that
collect your data so you have to trust them to really remove the identifiable
records and secondly how anonymous is anonymize data really in 2006 Netflix
started a competition called the Netflix price competing teams had to create an
algorithm that could predict how someone would rate a movie to help with this
challenge Netflix provided a dataset containing
over 100 million ratings submitted by over 480 thousand users for more than 17
thousand movies Netflix of course anonymized this data set by removing the
names of users and by replacing some ratings with fake and random ratings
even though that sounds pretty anonymous it actually wasn't to computer
scientists from the University of Texas published a paper in 2008 that said that
they had successfully identified people from this data set by combining it with
data from IMDB these types of attacks are called linkage attacks and it
happens when pieces of seemingly anonymous data can be combined to reveal
real identities another more creepy example would be the case of the
governor of Massachusetts in the mid-1990s this
it's Group Insurance Commission decided to publish the hospital visits of state
employees they anonymized this data by removing names addresses and other
fields that could identify people however computer scientists Latanya
Sweeney decided to show how easy it was to reverse this she combined the
published health records with voter registration records and simply reduced
the list there was only one person in the medical data that lived in the same
zip code had the same gender and the same date of birth as the governor thus
exposing his medical records in a later paper she noted that eighty-seven
percent of all Americans can be identified with only three pieces of
information zip code birthday and gender so much for anonymity clearly this
technique isn't enough to protect our privacy differential privacy on the
other hand neutralizes these types of attacks to explain how it works let's
assume that we want to get a view on how many people do something embarrassing
like for example picking their nose to do that we set up a service with the
question do you pick your nose and with the sno buttons below it we collect all
these answers on a server somewhere but instead of sending the real answers were
going to introduce some noise let's say that Bob is a nose picker and that he
clicks on the yes button before we send his response to the server our
differential privacy algorithm will flip a coin
if it's heads the algorithm sends Bob's real answer to our server if its tail's
the algorithm flips the second coin and sends yes if it's tails or no if it's
heads back on our server we see the data coming in but because of the added noise
we can't really trust individual records our record for Bob might say that he's a
no speaker but there is at least a one in four chance that he's actually not a
no speaker but that the answer was simply the effect of the coin toss that
the algorithm performed this is plausible deniability you can be sure of
people's answers so you can judge them on it this is particularly interesting
if you're collecting data about illegal behavior such as drug use for instance
now because you know how the noise is distributed you can compensate for it in
with a fairly accurate view on how many people are actually know speakers now of
course the coin toss algorithm is just an example and a bit too simple
real-world algorithms use the Laplace distribution to spread data over a
larger range and increase the level of anonymity in the paper the algorithmic
foundations of differential privacy it is noted that differential privacy
promises that the outcome of a survey will stay the same
whether or not you participate in it therefore you don't have any reason not
to participate in the survey you don't have to fear that your data in this case
your nose-picking habits will be exposed all right so now we know what
differential privacy is and how it works but let's take a look at who is already
using it Apple and Google are two of the biggest companies who are currently
using it Apple started rolling out differential privacy in iOS 10 and Mac
OS Sierra they use it to collect data on what websites are using a lot of power
what images are used in a certain context and what words people are typing
that aren't in the keyboards dictionary Apple's implementation of differential
privacy is documented but not open-source Google on the other hand has
been developing an open-source library for this the user in Chrome to do
Studies on browser malware and in maps to collect data about traffic in large
cities but overall there aren't many companies who have adopted differential
privacy and those who have only use it for a small percentage of their data
collection so why is that well for starters differential privacy
is only usable for large data sets because of the injected noise using it
on a tiny data set will likely result in inaccurate data and then there is also
the complexity of implementing it it's a lot more difficult to implement
differential privacy compared to just reporting the real data of users and
anonymize it in the old-fashioned way so the bottom line is that differential
privacy can help companies to learn more about a group of users without
compromising the privacy of an individual within that group adoption
however is still limited but it's clear that there is an increasing need in ways
to collect data about people without compromising their privacy
that's it for this video if you want to learn more head over to the simply
explained playlist to watch more videos and as always thank you very much for
watching
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