Corporate Surveillance in the Age of Digital Tracking, Big Data & Internet of Things
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Analytische MengeWeb ServicesSystemplattformServiceorientierte ArchitekturEntropie <Informationstheorie>ErwartungswertOnline-SpielSoftwareentwicklerUnternehmensmodellApp <Programm>TemperaturstrahlungFacebookDichte <Physik>Abstimmung <Frequenz>TabelleSchwebungVerkehrsinformationTeilkörperSoftwareDigitalisierungXMLComputeranimationBesprechung/Interview
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Transkript: Englisch(automatisch erzeugt)
00:03
Thanks a lot. Hi, everybody. My name is Wolfie Crystal, as she said, and as you can see,
00:25
and today I'll speak about how thousands of companies are tracking more and more aspects of our everyday lives, largely without our knowledge. It's not only governments who are spying on us. Today, we're constantly getting analyzed, categorized and rated by
00:41
global network of online platforms, ad servers, app developers, analytics companies, data brokers and many more, whose business models are based on the expectation of our personal data. During the last few years, I tried to find out how to beat this challenge. My first
01:02
try looked like that. I was asking, worried about your privacy? Forget it. Turn the tables and find out all the dirty details about your friends, your neighbors and the rest of the world. I co-created Data Dealer, an online game, even a Facebook game, about
01:20
collecting and selling personal data. Maybe somebody heard of it. You can still play it online. Since then, I did lots of research about digital tracking, data brokers and today's personal data ecosystem. End of 2014, I published an extensive 100-pages research report about corporate surveillance in the digital age. Recently, I've been contributing to
01:45
the Do Not Track project, an interactive web documentary series about digital tracking. And today, I'll speak about some of my research results. But first of all, I'm curious, who in the audience has ever been to psychotherapy? Mental illness and so on?
02:05
Come on, don't be shy. You've got nothing to hide. According to the statistics, you should be more, but no problem. We'll get that done. Ever heard of the big five? The big five for the five-factor model is one of the most popular personality models
02:24
in psychology. According to this, every human being can be described by using five main dimensions. Neuroticism, neurotic people are nervous, emotionally unstable, afraid of everything. Extroversion, openness to experience, conscientiousness and agreeableness. This
02:43
is about cooperation and social harmony. The five-factor model is well established. It has been used in research in more than 3,000 academic papers since many years. Now, a research team from Switzerland did an experiment. They asked 83 persons to fill
03:01
out a survey and use the classical empirical approach to calculate the values of each of these five personality traits, for example, like that. In the second step, they asked all of them to install an app on their phone, which collected some data about their phone usage. It recorded the number of uses of each of these apps during eight months.
03:25
They tracked the number and duration of incoming, outgoing and missed calls, and the same for short messages. Metadata, not content. Then they analyzed all this data and tried to find patterns. They tried to calculate the big five personality traits out of this
03:43
phone-locked data, and they were successful. They found statistical correlations not very strong ones, but still correlations. For example, they found out people who have used the Office and Calendar apps more often are rather neurotic, less open for new experiences, and when
04:02
it comes to the agreeableness, also quite bad. Heavy internet users on the phone, less extroverted and less open. Watching videos and listening to music, not very consciousness. That doesn't mean that it's always like that, but it's a higher probability. People receiving
04:21
many incoming calls, less neurotic, and they're probably extroverts. The longer the duration of somebody's incoming calls, the more extroverted, open and agreeable. Many missed calls, lack of openness. And finally, when somebody's calling lots of different people, it seems like this person could be super social. Okay, but if somebody prefers
04:43
to send messages instead of calling, sounds like both bad agreeability and bad conscientiousness. Okay, now you could tell me, yes, we already knew most of that, but that is not what this is about. This is about big data. Whoever is able to access these phone logs can do
05:04
these predictions for millions of phone users. Finally, when they try to predict someone's personality just based on smartphone metadata, for example, is somebody low or high in neuroticism. If you would guess, you'd get a prediction accuracy of 50%, but if they use the prediction
05:23
model based on phone data, they did quite better. By the way, I'm totally open, agreeable, extroverted and neurotic. No, just joking. But I finally found out the truth about
05:42
me with the help of five labs. They're analyzing your Facebook postings to predict your personality and the personality of your friends. Check it out if you want at your own risk. They've also calculated big five personality profiles for some VIPs. Mark Zuckerberg, 82% neuroticism. I don't know. Or Wikipedia's
06:06
Jimmy Wells, only 39% on openness. Shocking. But okay, it's based on just 150 views. But there are also other parties interested in the big five. This is a presentation slide
06:22
from the British Intelligence Agency, from the Snowden leaks. It seems that here they are thinking about how the big five personality traits correlate to the web browsers used. For example, they found out the Firefox users could be more neurotic. What they didn't analyze
06:41
in the research paper I mentioned is who is calling whom, who do you know, who are your friends. Social network analytics also offers powerful ways to make predictions about people. This is also just metadata. Metadata is powerful. Or like the former CIA director said, we kill people based on metadata. Or listen to his homie from the NSA. He said,
07:05
metadata absolutely tells you everything about somebody's life. If you have enough metadata, you don't really need content. Okay, this is not so much about corporates or Waylands. So to make it a bit less dramatic, this is Signify, or Signify, I don't know.
07:23
A US startup calculating credit scores from mobile phone call records. They say scores are created from behavioral and time-related attributes. No traditional payment data required. And four weeks of calling history is enough. So what is this all about?
07:41
It's all about data mining. Using methods like cluster analysis and so on, involving methods from mathematics and statistics to machine learning. Machine learning algorithms try to learn from existing data. They get trained to find correlations in large data sets. And they're
08:01
able to find patterns where human beings with their traditional statistical approaches really give up. For example, there's this company founded by a former Google employee called Test Finance. Again, about credit scoring, they say this is the math we all learned at Google. A
08:24
good the grammar was, or which font they used, or when it was edited or created, everything. Now Test Finance is doing the same for credit scoring. They're combining 70,000 signals to calculate personal credit scores. And they say all data is credit data. We just don't know
08:44
how to use it yet. All data, there's lots of data about us out there. Some of you probably know this. This is Lightbeam, a browser extension, which shows who is watching us when we're surfing the web. Here I visited five websites, the New York Times, Guardian, BuzzFeed, Wires and
09:04
Dictionary.com. In the background, these five sites connected to 104 other third-party services and told them about my visit. This is how they are able to compile profiles about my online behavior. And there are tens of thousands of tracking companies doing this stuff.
09:23
But it's not just surfing the web. There's also that special device I already mentioned. Our smartphone is a powerful, small computer with plenty of different sensors under the hood, even if it's a bit broken, like this one. Our phone contains lists of our contacts,
09:44
friends, of our calls, our messages. It tracks our movements, lots of very private data. Now, who is able to access this data? Mobile phone networks, platform providers like Google or Apple, and app developers. But there are also many hidden third-party services included in many
10:02
apps. For example, Flurry, a mobile analytics platform. They're basically helping app developers to earn money. And additionally, they're monitoring the behavior of users in more than 500,000 apps on 1.4 billion smartphones and tablets. They're accessing attributes like gender, age, language,
10:23
kind of device or tracking system, and so on. And they're also sorting uses into categories like hardcore gamers, financial geeks, new mothers, or even on categories based on their sexual orientation. But wait, how is Flurry able to offer information about the sexual orientation
10:41
of people they're tracking? Maybe they're using similar algorithms than the researchers of this Berkeley study. They tried to predict private attributes of users just based on their Facebook likes. And these were the results. They were able to successfully predict gender,
11:01
sexual orientation, political, and religious views, just based on about 170 Facebook likes per user. And our likes are a special category of data, very similar to the websites we visited, the products we bought, and so on, or the apps we're using. By the way, in 2014,
11:22
they established a partnership with a market research and consumer data company. Now they're offering additional 300 offline data points about family background, income, and so on. OK, smartphones. And then some people started this special kind of movement some years ago.
11:41
They called it the quantified self. This guy seems to be one of the early adopters. Not everybody involved has got this kind of tentacles on the head. Others are, for example, using body media, my GPS to help their life, really fancy slogans for advertising.
12:02
I love it. These devices are tracking your activity, your steps, calories, heartbeats, and so on. And this is an example report. Very important, you can enter target values, for example, 10,000 steps, and then you try to reach that gamification.
12:20
As you can see, this person is in a weight loss trend, great, but only four hours of sleep, I would say, not so good. However, another wonderful ad from body media I love, that it records everything from sunrise to sunset and beyond. OK, these ads look a bit old school, maybe because they're from 2012. Today they're
12:49
from the US. In the meantime, they got acquired by Jawbone, one of the market leaders in the field of fitness trackers, another market leader is Fitbit, very stylish. But if you move to
13:01
their corporate wellness section on their website, you can see they've already got partnerships with several big companies. For example, with the oil company BP. The employees can choose to join the Fitbit corporate wellness program. If they reached one million steps in a year,
13:20
they got a discount on the health insurance premium. BP self-insures, so their employees don't have any other health insurance. According to Bloomberg, one of the employees got a discount of $1,200 on his annual insurance bill in 2013, when he reached his one million steps.
13:41
I mean, he didn't have to join the program. But let's say $1,200. This is not just a tiny little incentive. For many people, this amount would make it mandatory to participate. And this is maybe what the famous internet guru Timurélí meant when he was talking about,
14:01
you know the way that advertising turned out to be the native business model for the internet. I think that insurance is going to be the native business model for the Internet of Things. And yes, it's not only smartphones or fitness trackers. In the upcoming Internet of Things, more and more objects will have sensors. A bit smaller ones than this, but
14:25
sensors and network connections from e-book readers and smart TVs to connected cars, smart meters, thermostats, and much more. These objects will be everywhere. At home, at work, and in public space. And it won't just be people tracking themselves. It will also
14:43
help tracking other people. For example, the children or the employees who could either carry some devices with them or stay at places where sensors are installed. Many experts expect that the major driver of the Internet of Things will be incentives to get people
15:03
change their behavior. Maybe to purchase something, or to act in a more healthy or safe manner, or maybe to work in a specific way. For example, more quickly. During the last few years,
15:20
many car insurance companies already started to offer policies which are based on tracking the driving behavior of people. They're constantly measuring the car's speed and location, and then they're calculating a score. This is an example app. If you drive carefully, you get a discount on your insurance premium. And most of these programs will lower your score
15:45
if you're accelerating fast or braking hard. But wait, no harsh braking? Really? I think this shows one of many problems of incentive systems like this. Of course,
16:01
it's not only phone locks, car driving behavior, health data being collected. Today, virtually everything we do is recorded, monitored, or tracked in some way. All kinds of collected data ends up in big clusters of databases. Data mining technologies help to find the relevant information in these massive amounts of data. But there's one US company who has
16:25
probably collected the largest amount of personal information about consumers on the planet. It's called Axiom. Axiom collected up to 3,000 attributes on 700 million people. For example,
16:41
they've got credit history, driving history, criminal history, purchase behavior, voter party, health interests, and much more. They run a YouTube channel full of really nice videos. I can't resist to show a short part. Pay attention how beautiful they use their X.
17:02
Ah, there is no sound, but I use my microphone. No, they use the X. Enough. Axiom is only one of a couple of major companies in personal data programming. Another one would be LexisNexis Risk Solution. Also very nice name. They've got a similar
17:24
amount of data about consumers and a really impressive range of offers. They're not only selling data on problem renters. They're also offering employment screening, make quicker, confident pre- and post-hiring decisions. And they've also got offers for
17:45
healthcare. Here they say social networks analytics reveal hidden relationships. They even write somewhere on the website, we help predict the likelihood that the consumer will become delinquent in the next 18 months. Or take, for example, social intelligence.
18:02
They're promising also to drive intelligent decisions based on data from social media. They're offering products in the fields of insurance, employment, and much more. Two more fancy examples in the fields of the military digital complex, as I'd like to call it. This is Palantir, a Silicon Valley data mining company. They're offering products for companies
18:25
in the fields of healthcare, insurance, and finance. Their software is based on Paypal's fraud detection algorithms. And they've got partnerships with SAP, the US Department of Defense, and with the CIA. Or Recorded Future, I also love that name. They're offering smarter
18:46
business decisions with web intelligence. And they're working for the US military and intelligence agency, as well as for big corporations. So this is the horror show of personal data usage, I would say. But also LexisNexis offers data to governmental agencies.
19:04
Look at the nice black helicopter. Axiom is not so much into governmental clients, just a tiny little bit. Anyway, originally Axiom is in kind of old school data broker. They started 40 years ago with sending personalized letters, but they're trying hard
19:24
to connect to the digital world. In an Axiom presentation from 2012, I found on the internet, they ask, what if we could social data like Google does it? In 2015, they've got partnerships with Google, Facebook, Twitter, and many more. And like many other data brokers, they're now mainly
19:45
focusing on how to connect customer data from companies to online profiles. Or how to connect online and offline data about consumers, the so-called data onboarding. They want to make it possible to match in-store purchases in the supermarket or in other shops to people serving
20:03
the web or using their smartphones. Or more specific, to the devices they're using to cookie identifiers or to user accounts on platforms like Facebook. This is why they bought another company called LifeRamp in 2014 for 300 million dollars. But don't worry, everything is happening
20:21
totally secure and anonymous. They promised to use a process that protects consumer privacy, believe me. At the same time, one of the key features is one-to-one exact matching on your file. Which file? The customer data records file. So on the one hand, there is our online
20:41
behavior across device and across platform. On the other hand, every company has its customer database and its purchase data. And now they're trying to combine it. How are they able to do it? By matching, linking, and synchronizing our email addresses, phone numbers, and device IDs with cookie IDs and user accounts. For example, our Facebook user account. Of course, they also
21:04
use third-party consumer data from data brokers. This way, businesses in all industries are able to identify and profile customers also based on their online behavior. And this is, I believe, where things get really creepy. Because businesses in all industries don't
21:25
just use their data for marketing. They also use it for their whole customer relationship management workflow. They're calculating things like customer values and scoring is omnipresent in today's corporate world. Companies also love to implement risk management systems.
21:40
And the more data they get, the better. Most consumers have no idea that many of their everyday life interactions are affected by how they are categorized and rated by companies. Take, for example, Telepart, a so-called predictive marketing platform. Their slogan is, turn our science into your sales. They are offering a Telepart identity
22:04
network, which incorporates massive amounts of data from both online and offline sources to create a Telepart identity key for each and every shopper. And then they create a customer value score for every shopper and product combination.
22:21
As a result, some customers get personalized offers based on their online and even offline behavior. And by the way, a few days ago, Twitter bought Telepart for about $500 million. Personalized pricing could soon be everywhere. Big, global online shops already showed differently priced products for different users or even
22:43
the same products at different prices based on their online behavior, their location data, or the devices they use. A research paper from 2012 showed that personalized prices differed up to 166 percent. Michael Furtick, a U.S. privacy advocate and the founder of
23:03
Reputation.com, even said, the rich see already a different Internet than the poor. Based on their personal data, in some cases, we still have the choice. For example, Lando, their slogan is, change your life today. They use data from Facebook accounts to
23:21
course about users and to ensure you are who you say you are. And they're also analyzing the connections to your friends. So be sure to have valuable friends. You can use Lando to unlock loans, online shopping and employment opportunities. And
23:41
there's and then there's this nice Facebook looking button. In the case of Lando, you have no other loan you can get or if a Lando login is required for that specific shop application. They're all using such wonderful and innocent terms, scoring risk management,
24:03
predictive analytics, make better decisions. But what they really mean is surveillance. Persuasive and omnipresent surveillance based on digital tracking and our profiles. In the second step, they're offering us incentives followed up by testing different options again and again, which is an incentive is more successful. They call it increasing engagement,
24:26
improving conversion rates, optimizing business processes, motivating behavioral change. But what if I use a very old fashioned term and call it manipulation? Today, it's just about
24:41
personalized experience and targeted advertising. One person is waiting longer on the phone hotline because the revenue prediction for this person is too crappy or that specific product in the online shop is out of stock, but only for you because you're predicted to be too less profitable or too risky. In the future, similar things could happen in much more areas of our
25:06
lives. So surveillance was a hard and painful job some 30 years ago, but times are changing fast. I think we really have to be careful. A future society where digital monitoring is omnipresent could have massive impact on people's lives, options and behaviors. We really have to be
25:25
careful that information age doesn't end up in a kind of digital technocracy. That's why I'm working on these issues and that's why I published this report last November. Unfortunately, it's in German only, but I hope that I will be able to find an English version soon.
25:43
You can download it at crackedlabs.org. There's also at least a document containing the contents and references in English. So what has to be done? One option could look like that. This is not my laptop? No. I've been using the internet since the early 1990s, so I know that
26:07
at the same time information technology is great, but in the moment it's mainly companies and governments who are accessing our data. It's not balanced at all. Most of it is happening in a largely non-transparent way. Here's a quick summary of basic policy recommendations concerning
26:27
corporate data collection which resulted from a research report. I think the most urgent challenge is to make corporate data collection more transparent. After that, the never-ending story of the European data protection reform, I think we need it good to be good and we need
26:46
it soon. But even if we get the quality data protection regulation, I'm afraid this won't be enough. In the moment companies are not only in control of our personal data, they're even trying to shape our future information society. And they've got billions and billions of dollars.
27:04
I think we need much more support of decentralized privacy-aware technology. I'm having even a completely new industrial policy and billions for open source components and frameworks supporting another kind of innovation which is respecting our privacy.
27:20
And then there is this large group of people committed to this kind of another internet. I think it's crucial to make digital civil society much stronger and we need much more digital literacy. And so this is the end. The big man Eric Schmidt said,
27:46
you have to fight for your privacy or you will lose it. I'm asking friendly advice or a serious threat. I don't know. Thanks. I think we have time for one question while
28:09
we do the changeover. Does anybody have a question they'd like to ask? So I finished too fast now. One minute to spare. Anybody? Okay.