Lyon - Surveillance, Snowden, And Big Data

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Big Data & Society
http://bds.sagepub.com/content/1/2/2053951714541861
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DOI: 10.1177/2053951714541861
2014 1: Big Data & Society
David Lyon
Surveillance, Snowden, and Big Data: Capacities, consequences, critique
 
 
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Original Research Article
Surveillance, Snowden, and Big Data:
Capacities, consequences, critique
David Lyon
Abstract
The Snowden revelations about National Security Agency surveillance, starting in 2013, along with the ambiguous
complicity of internet companies and the international controversies that followed provide a perfect segue into con-
temporary conundrums of surveillance and Big Data. Attention has shifted from late C20th information technologies and
networks to a C21st focus on data, currently crystallized in ‘‘Big Data.’’ Big Data intensifies certain surveillance trends
associated with information technology and networks, and is thus implicated in fresh but fluid configurations. This is
considered in three main ways: One, the capacities of Big Data (including metadata) intensify surveillance by expanding
interconnected datasets and analytical tools. Existing dynamics of influence, risk-management, and control increase their
speed and scope through new techniques, especially predictive analytics. Two, while Big Data appears to be about size,
qualitative change in surveillance practices is also perceptible, accenting consequences. Important trends persist – the
control motif, faith in technology, public-private synergies, and user-involvement – but the future-orientation increasingly
severs surveillance from history and memory and the quest for pattern-discovery is used to justify unprecedented access
to data. Three, the ethical turn becomes more urgent as a mode of critique. Modernity’s predilection for certain
definitions of privacy betrays the subjects of surveillance who, so far from conforming to the abstract, disembodied
image of both computing and legal practices, are engaged and embodied users-in-relation whose activities both fuel and
foreclose surveillance.
Keywords
Surveillance, privacy, Big Data, control, ethics, Snowden
Introduction: Snowden disclosures and
Big Data
The Snowden revelations about National Security
Agency (NSA) surveillance, starting in June 2013,
along with the ambiguous complicity of internet com-
panies and the international controversies that fol-
lowed illustrate perfectly the ways that Big Data
has a supportive relationship with surveillance.
Words such as ‘‘bulk data’’ and ‘‘dragnet’’ and
‘‘mass surveillance’’ more than hint that processes
referred to as ‘‘Big Data’’ are in play, producing
expanded and intensified surveillance. The rapid and
widespread adoption of what are called Big Data
practices signal profound changes for individuals,
for the dynamics of both public and private sector
organizations, for the relation of citizen to state, and
for society at large.
However, for a fuller understanding of Snowden’s
revelations and Big Data surveillance several matters
have to be unpacked, not least the questions of the
socio-technical character of Big Data, how several of
the Snowden revelations demonstrate dependence on
Big Data techniques and which have a highly significant
impact for understanding the character of surveillance
today. Of course, some of what Snowden has revealed
involves targeting but the main focus here is on Big
Data techniques. Beyond this, it is vital to consider
what is meant by the controversial key concepts,
Queen’s University, Ontario, Canada
Corresponding author:
David Lyon, Queen’s University, University Avenue, Kingston, ON,
Canada K7L 3N6.
Email: [email protected]
Big Data & Society
July–September 2014: 1–13
! The Author(s) 2014
DOI: 10.1177/2053951714541861
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‘‘Big Data and surveillance.’’ What follows is a pro-
vocative introduction to some key issues raised by the
social and political realities of this conceptual conjunc-
tion, prompted by the Snowden revelations.
Big Data, first, may be best thought of as ‘‘the cap-
acity to search, aggregate and cross-reference large data
sets’’ (Boyd and Crawford, 2012: 663). There is of
course a range of ideas, practices, metaphors, software,
and techniques bundled together in those two decep-
tively straightforward-sounding words. For one thing,
Big Data practices occur in a variety of contexts
(Meyer-Scho¨ nberger and Cukier, 2012) and one big
mistake is to imagine that similar kinds of ends and
possibilities of success are in view whatever the context.
Consumer marketing, health care, urban policing, and
anti-terrorism – to take four popular potential and
actual application sites for Big Data – are not the
same and practices that may in some respects be accept-
able in one (say, marketing) may erode rights and deny
human dignity in another (say, anti-terrorism) (Mosco,
2014: 177–205). If there are potential benefits or harms,
that is, they are not the same in each area.
The second crucial concept is surveillance, that can
be understood as any systematic, routine, and focused
attention to personal details for a given purpose (such
as management, influence, or entitlement; see Lyon,
2007: 13–16). This too is a broad definition that needs
some tightening for the present purpose. Our task here
is to examine how far Big Data intensifies certain sur-
veillance trends associated with information technolo-
gies and networks (see Bennett et al., 2014), and is thus
implicated emerging configurations of power and influ-
ence. Of course, as political-economic and socio-tech-
nological circumstances change, so surveillance also
undergoes alteration, sometimes transformation.
Classically, studies of surveillance suggest that a shift
in emphasis from discipline to control (Deleuze, 1992;
Haggerty and Ericson 2000) has been a key trend asso-
ciated with the increasing use of networked electronic
technologies that permit surveillance of mobile popula-
tions rather than only those confined to relatively cir-
cumscribed spaces, and depend on aggregating
increasingly fragmented data. Surveillance practices
have been moving steadily from targeted scrutiny of
‘‘populations’’ and individuals to mass monitoring in
search of what Oscar Gandy calls ‘‘actionable intelli-
gence’’ (2012: 125) and Big Data surveillance exempli-
fies this.
Two main questions are addressed here: One, in
what ways and to what extent do the Snowden disclos-
ures indicate that Big Data practices are becoming
increasingly important to surveillance? Queries about
Big Data practices in relation to surveillance and
public concern about the activities of the NSA predate
Snowden, of course (Andrejevic and Gates, 2014).
But Snowden’s revelations have brought them into
the public eye as never before. Two, if Big Data is
gaining ground in this area, then how far does this indi-
cate changes in the politics and practices of surveil-
lance? Are new trends, or the augmentation of older
ones, visible here? We shall explore these questions in
respect to the capacities of Big Data and their social-
political consequences before commenting on the kinds
of critique that may be appropriate for assessing and
responding to these developments.
The Snowden disclosures
The first item revealed by Edward Snowden on 6 June
2013 and published in The Guardian (UK) was that the
NSA, using an order from the Foreign Intelligence
Surveillance Court (FISC), had required the telecom-
munications giant Verizon to hand over metadata from
millions of American’s phone calls to the Federal
Bureau of Investigation and the NSA (Greenwald,
2013). Verizon itself was forbidden to disclose to the
public either the order or the request for customer
records.
The next day, articles in the Washington Post and
The Guardian detailed how the PRISM program
seemed to give the NSA direct access to the servers of
some of the biggest technology companies, including
Apple, Facebook, Google, Microsoft, Skype, Yahoo,
and YouTube. Encryption and privacy controls were
circumvented with the help of the companies
(Gellman and Poitras, 2013). In the UK, the Tempora
program appeared to be even more like a dragnet as it
gave similar access to GCHQ (General
Communications Headquarters; the UK partner of
the NSA in the ‘‘Five Eyes’’). Together, their cable
and network tapping abilities are called ‘‘Upstream’’
and can intercept any internet traffic. The database
that allows the information to be extracted in real
time is called ‘‘XKeyscore’’ (Lanchester, 2013). The
revelations have continued and Snowden himself
has said (in early 2014) that some of the most striking
disclosures are yet to come.
The surveillance practices revealed by Snowden
show clearly if not completely that governments –
especially American, British, Canadian, and possibly
other agencies – engage in astonishingly large scale
monitoring of populations, and also how they do it.
On the one hand, the NSA engages contractors to
share the burden of their work and also gathers and
mines user data collected by other corporations, espe-
cially telephone, internet, and web companies. And on
the other, this kind of surveillance also means that the
NSA and similar agencies watch for cookies and log-in
information. They thus use data derived from the use of
devices such as cell phones or geo-locating social media
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sites. What users unknowingly disclose on those plat-
forms – such as Facebook or Twitter – or when using
their phones, is usable data for ‘‘national security’’ and
policing purposes. But more importantly from a Big
Data perspective, metadata (see the discussion below)
relating to users is gleaned without their knowledge
from the simple use of these machines. There are thus
at least three significant actors in this drama, govern-
ment agencies, private corporations and, albeit unwit-
tingly, ordinary users.
What holds these groups together, in a sense, is the
software, the algorithms, the codes that allow users’
data to be systematically extracted or disclosed, ana-
lyzed, and turned into what the data collectors and
others, such as the NSA, hope will be actionable
data. In other words, it is the (big) data practices that
different kinds of operations have in common. As
Snowden himself said in a 10 June 2013 video, the
‘‘ . . . NSA targets the communications of everyone . . . ’’
then ‘‘ . . . filters, analyzes, measures them and stores
them for periods of time simply because it’s the easiest,
most efficient and most valuable way of achieving these
ends’’ (Greenwald et al., 2013). The NSA thus depends
on codes, the algorithms, plus the witting or unwitting
cooperation of both telephone and internet corpor-
ations in order to do surveillance. Individual users
may play a part, too, but their role is hardly one of
conscious actors in the drama. This already goes
beyond what many once imagined was direct and spe-
cifically targeted relationships by state agencies of indi-
viduals, to mass surveillance, dependent on a close
liaison with corporate bodies and on the self-recording
devices used in everyday communications and
transactions.
The gathering of national intelligence in the U.S. is a
mammoth undertaking, worth over US$70 billion per
year (FAS, 2014) and involving extensive links with
universities, internet companies, social media, and out-
side contractors – such as Booz Allen Hamilton that
employed Edward Snowden and from which Snowden
illegally conducted his removal of sensitive data. If
nothing else, the economic value of these operations
indicates how much emphasis is placed on data process-
ing by government agencies and in turn by global cor-
porations. But what kinds of data are sucked up so
voraciously by these organizations with such sophisti-
cated processing power?
The word that has perhaps appeared most in relation
to the Snowden revelations is ‘‘metadata.’’ This term
refers – rather imprecisely – to the ‘‘data about data’’
such as the IP address, the identity of the contact, the
location of calls or messages, and the duration of the
contact. However, metadata takes many forms, well
beyond communications. For example, automatic
license plate recognition systems or word-processing
programs also generate metadata (Newell, forthcom-
ing). While specific cases of monitoring the content of
phone calls and examining text messages exist as well,
the extremely large-scale collection and analysis of
metadata characterizes many of the disclosures about
the kinds of activities with which the NSA is engaged.
When the Snowden revelations began in June 2013,
governments and agencies were quick to dismiss them
by downplaying the significance of metadata.
In the U.S., the collection of metadata was permitted
after 9/11 under the ‘‘Section 215 Bulk telephony meta-
data program’’ but it is unclear how far similar such
programs extend to other countries such as Canada or
the UK. However, it was revealed in 2014 that the
Canada Border Services Agency made 19,000 requests
for subscriber data in one year but this and other
related Canadian agencies are under no statutory
requirement to say how often such requests are made
or for how much data (Freeze, 2014). More specifically,
a program that featured in the news media as Canada’s
CSEC (Communications Security Establishment of
Canada) collecting data from airport Wifi systems
was actually a general means of identifying travel pat-
terns and geographic locations using ID data (that is,
metadata) in conjunction with a database of IP
addresses supplied by the company Quova over a
two-week period in January 2014. What this shows is
how data are analyzed, rather than just the fact of its
collection (Schneier, 2014). Such data may be used, for
instance, to set up an alarm when a ‘‘suspect’’ enters a
particular hotel, or to check on someone – a kidnapper,
maybe – who may have repeatedly visited a particular
location. But it takes little imagination to think of other
potential uses for such datasets.
This is why security critic Bruce Schneier cuts
through the obfuscations to state unequivocally that
‘‘metadata is surveillance.’’
1
As he also observes,
while the mass media accounts focus on what surveil-
lance data are being collected, the most significant ques-
tion is how the NSA analyzes those data. On the one
hand, the nearly five billion cell phone records collected
by the NSA each day by tapping into cables that con-
nect mobile networks globally can reveal personal data
about where users are located, anywhere in the world.
The NSA can attempt to track individuals to private
homes and can also retrace earlier journeys, whenever
the phone is on, because phones transmit location data
whether or not they are in use. On the other hand, the
NSA also analyzes patterns of behavior to reveal more
personal information and relationships between differ-
ent users (Gellman and Soltani, 2013). The latter is
more subtle, but in the Big Data world, more
significant.
These pattern-seeking processes are the ones where
Big Data practices really come into their own.
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For example, the NSA program, known as
‘‘Co-Traveler,’’ uses highly sophisticated mathematical
techniques to map cell phone users’ relationships,
superimposing them on others to find significant inter-
sections and correlations. Co-Traveler is meant to
search for the associates of foreign intelligence targets,
although domestic users’ data are also garnered ‘‘inci-
dentally’’ and the foreign sweeps are so broad that they
are bound in include Americans on a mass scale. This is
the searching, aggregating, and cross-referencing pro-
cess referred to above, that characterizes some of the
technical aspects of Big Data.
Several key surveillance trends (see Bennett et al.,
2014) are augmented by the advent of Big Data. Just
two are mentioned here. One is that contemporary sur-
veillance expands exponentially – it renders ordinary
everyday lives increasingly transparent to large organ-
izations. The corollary, however, is that organizations
engaged in surveillance are increasingly invisible to
those whose data are garnered and used. This ‘‘para-
dox’’ is deepened by the advent of Big Data (Richards
and King, 2013). A second trend is that the expanding
securitization of daily life prompts the use of extended
surveillance, from neighborhoods and travel arrange-
ments to large sporting and entertainment events. The
quest of ‘‘national security’’ breeds Big Data, particu-
larly through efforts to preempt security breaches by a
form of anticipatory surveillance described, somewhat
vaguely, by the Department of Homeland Security as
‘‘connecting the dots.’’
Of course, the surveillance implications of the use of
Big Data – such as using metadata – are just one dimen-
sion of new ways of structuring information in a digital
age. The present task is not to catalogue potentially
beneficial aspects of Big Data but rather to focus atten-
tion on what sorts of surveillance issues are raised –
especially ones that prompt civil liberties or privacy
questions – in new ways by this re-structuring of
information.
Big Data surveillance
The Big Data/surveillance link was recognized by US
President Obama on 17 January 2014, when he called
for a ‘‘comprehensive review of Big Data and privacy’’
following the Snowden leaks (White House, 2014). It
was further acknowledged when the US proposed new
rules governing bulk data collection by the NSA of the
phone calling habits of Americans (Savage, 2013). The
once-secret bulk phone records problem was what had
most alarmed privacy advocates when the Snowden
leaks began in 2013 and now the president proposed
that it should be curtailed, along the lines of a dated
European data retention directive. But the media rhet-
oric surrounding this suggests a fairly conventional
understanding of surveillance that does not fully
grasp the Big Data aspects of the bulk phone records.
Surveillance constantly undergoes change and is cur-
rently being reconfigured in several respects, some of
which alter its character. In particular, different kinds
of data are now being captured and used in new ways,
which prompts some to distinguish between surveil-
lance as targeted practices over against fresh form of
dataveillance (van Dijck, 2014). Not only are data cap-
tured differently, they are also processed, combined,
and analyzed in new ways. Social media that appeared
on the scene at roughly the same time as responses to 9/
11 boosted the ‘‘surveillance state,’’ are now the source
of much data, used not only for commercial but also for
‘‘security’’ purposes. The buzzword is ‘‘datafication,’’
which points to the ways that for many businesses,
the information infrastructure is their heart
(Bertolucci, 2013). Ordinary users’ social activities are
sucked up as data, quantified and classified, making
possible real-time tracking and monitoring.
It goes beyond this, however. With Big Data prac-
tices, for example, personal data – now including iden-
tifiable metadata – are not collected for certain limited,
specified, and transparent purposes, which are the goals
of data protection and privacy advocates. Rather, Big
Data reverses prior policing or intelligence activities
that would conventionally have targeted suspects or
persons of interest and then sought data about them.
Now bulk data are obtained and data are aggregated
from different sources before determining the full range
of their actual and potential uses and mobilizing algo-
rithms and analytics not only to understand a past
sequence of events but also to predict and intervene
before behaviors, events, and processes are set in
train. Both corporate and government aspects of this
raise questions for analysis and critique.
Preemptive approaches in security and policing, that
depend on prediction, have been growing steadily since
the 1990s and were extensively augmented after 9/11,
are a bureaucratic incentive to over-collect data, espe-
cially in security and law enforcement. Perhaps even
more important to cost-cutting government depart-
ments, the falling cost of processing power is a strong
inducement to use new data analytics in a number of
fields (Bankston and Soltani, 2014). It is not hard to
find extravagant promises that real-time data analytics
will transform aspects of retail, manufacturing, health
care, and public sector organizations. But despite the
determined and well-informed activities of data protec-
tion and privacy advocates over a number of years and
in several countries, any countervailing focus on the
contribution Big Data may make to reducing demo-
cratic freedoms, reconfiguring privacy and indeed, rede-
fining the role of information in contemporary life, is
still muted and marginalized.
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Thus stated, the problem is that basic alterations in
surveillance and legal expectation are occurring in a
context that celebrates rather than carefully assesses
Big Data. The differences between Big Data applica-
tions are crucial here. For example, Ian Kerr and
Jessica Earle (2013) distinguish helpfully between
three kinds of prediction: consequential, where one
aim is to help clients or users to choose what is likely
to be beneficial to them, preferential, illustrated by mar-
keters trying to second guess our desires from our
browsing behavior, and preemptive, where is a deliber-
ate intention to reduce someone’s range of options. In
the context of law and justice, the latter raises funda-
mental issues of privacy and due process. Where legal
systems are based on an after the fact system of penal-
ties or punishments, the turn to one based on future-
oriented preventative measures is of huge import, not
least for those rendered unable to understand or con-
tribute meaningfully to the process.
This is why the Snowden revelations offer a unique
opportunity to grapple with Big Data surveillance in a
systematic way. Of course, this phenomenon did not
appear overnight, fully formed. It represents the con-
fluence of many streams and is itself better thought of
as a fluid form that constantly changes its character
than as a relatively solid set of surveillance relations
that positions and governs the subject in a disciplinary
fashion. Introducing the language of surveillance to Big
Data discussions challenges the practices often
described in epistemologically naı¨ve and politically dis-
ingenuous ways. But equally, examining how Big Data
practices are affecting the character of contemporary
surveillance obliges students of surveillance to recon-
sider what is happening, particularly across different
surveillance domains.
Capacities
The term ‘‘Big Data’’ suggests that size is its key fea-
ture. Massive quantities of data about people and their
activities are indeed generated by Big Data practices
and many corporate and government bodies wish to
capitalize on what is understood as the Big Data
boom. As with many other single aspects of this phe-
nomenon, however, the idea of size both yields import-
ant clues and, on its own, can mislead. While the
capacities of Big Data practices (including the use of
metadata) intensify surveillance by expanding intercon-
nected datasets and analytical tools, this tells only a
part of the story.
Drawing on a number of sources, Rob Kitchin
argues that Big Data has several crucially important
characteristics: huge volume, consisting of terabytes
or petabytes of data; high velocity, being created in or
near real time; extensive variety, both structured and
unstructured; exhaustive in scope, striving to capture
entire populations of systems; fine-grained resolution,
aiming at maximum detail, while being indexical in
identification; relational, with common fields that
enable the conjoining of different data-sets; flexible,
with traits of extensionality (easily adding new fields)
and scalability (the potential to expand rapidly)
(Kitchin, 2014: 262).
Data sources may be thought of under three main
headings each of which may be applied in surveillance
contexts: directed, automated, and volunteered
(Kitchin, 2014, forthcoming). In the first, a human
operator obtains the data, obvious examples being
CCTV systems or police seeking, say, vehicle ownership
records. In the second, the data are gathered without a
human operator intervening; traces are recorded rou-
tinely from transactions with banks or consumer out-
lets and communications, using cellphones above all. In
the third, data are in a weak sense ‘‘volunteered’’ by the
user who gives out information on social media sites
and the like. Of course, social media users do not neces-
sarily think of their activities in terms of volunteering
data to third parties (Trottier, 2012) but this is an
accurate way of understanding surveillance data gath-
ering in this context.
Clearly, one of the surveillance trends amplified by
Big Data practices is the increased integration of gov-
ernment and commercial surveillance. Big Data may
also be thought of in terms of its promised economic
rewards. As Bruce Schneier observes, the term Big Data
could be viewed as placing today’s data operations in
the same kind of category as ‘‘Big Pharma’’ or ‘‘Big
Oil’’ (Schneier, 2012), where the corporate strategy
behind the new practice is the decisively significant
factor and when ‘‘big’’ refers to the economic worth
of the data commodity. This dimension is very import-
ant to any analysis, as Viktor Mayer-Scho¨ nberger and
Kenneth Cukier (2012) show. They argue that the ‘‘Big
Data revolution’’ is based in part on new data manage-
ment techniques that permit analysis beyond ‘‘rows and
tables’’ to dispensing with ‘‘hierarchies and homogen-
eity’’ but also on internet companies collecting vast
troves of data and having ‘‘a burning financial incentive
to make use of them’’ such that they became leading
users of the latest processing technologies, sometimes
superseding others that had decades more experience
(2012: 6).
Understood thus, the capacities of Big Data surveil-
lance take on some new meanings. The enthusiasm for
commercial uses of Big Data is shared by those in the
security field, thus stimulating further integration of
these activities. In a Big Data context, the same data
are increasingly used for different purposes. This is
more than a change of context that might alter how
data subjects might construe their privacy or how
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legal limits on secondary use might be stretched.
Rather, the same commercial data may be given new
meanings in the security realm, combined, and con-
nected in novel ways. Thus the capacities of Big Data
may also be seen to allow new forms of inferential rea-
soning that Louise Amoore calls ‘‘data derivatives’’
(2011). This is of critical importance in the security-
surveillance context because such associations and
links, however trivial and improbable, may be given
new meanings that are cut off from the values that
once made sense of them and the identifiable subjects
whose activities generated them in the first place.
Consequences
As with the capacities of Big Data, given the current
volatility of the field it is hard to tell exactly what the
consequences of widespread adoption of Big Data will
be for surveillance. Important trends will probably per-
sist, including the quest for control through surveil-
lance, an almost naı¨ve faith in technology that
inhibits the search for low-tech or no-tech alternatives,
public-private synergies that benefit government, cor-
poration – and sometimes citizens – and the involve-
ment of internet users in surveillance processes as
‘‘prosumers.’’ At the same time, the reinforced future-
orientation is likely to exacerbate the severance of sur-
veillance from history and memory and the assiduous
quest for pattern-discovery will justify unprecedented
access to data. It is likely that existing dynamics of
influence, risk-management, and control will increase
their speed and scope through new techniques, espe-
cially predictive analytics, but what specific trends will
be accentuated as Big Data practices expand?
There are three key ways in which commitment to
Big Data practices seem to be shifting the emphasis of
surveillance and this is clear in the Snowden disclos-
ures. They are stated here and discussed below. First,
given that Big Data involves the amplified use of algo-
rithms for analytics, an increasing reliance on software
for surveillance and a concomitant reliance on what
might be called a ‘‘human-algorithm’’ relationship
that shapes the ways that human subjects are treated
by surveillance systems. Such automation tends to
diminish opportunities for discretion within systems.
Second, Big Data practices increasingly tilt surveillance
operations to focus on the future more than on the
present and the past. In the context of neo-liberal gov-
ernance, this anticipation is likely to place more weight
on surveillance for managing consequences rather than
research on understanding causes of social problems
such as crime and disorder. The third area is adaptation,
the propensity for analytics to be treated as if methods
can be transferred successfully and with little risk from
one field to another. The enthusiasm for Big Data
‘‘solutions’’ may lead to the inappropriate transfer of
techniques from one field to another.
Automation
The combination of readily available software and its
relatively low price is an incentive to choose technical
solutions over more labor-intensive ones in surveillance
practices as in other fields (see Bankston and Soltani,
2014 on how this affects police location tracking). This
means that automated surveillance will become an
increasing possibility. At the same time, greater data
storage capacity means that larger and larger amounts
of data are collected before their use has been ascer-
tained (Savage and Burrows, 2007), the consequences
of which are unknown as yet. What we do know, how-
ever, is that who makes decisions about algorithms and
datasets will have the capacity to make a difference in
these emerging scenarios (Glennon, 2014).
The automation of surveillance must also be seen as
an aspect of the way that surveillance occurs as a rou-
tine management procedure. Evelyn Ruppert rightly
warns against panoptic panics regarding government
surveillance that suggest sinister state attempts to
keep close watch on all citizens. The automating of
surveillance is part of the kinds of cost-cutting and effi-
ciency exercises that have dominated the public admin-
istration for decades. So far from there being an ‘‘all-
knowing state, what we have instead is a plethora of
partial projects and initiatives that are seeking to har-
ness ICTs in the service of better knowing and govern-
ing individuals and populations’’ (Ruppert, 2012: 118).
And if the process is not panoptic then it is not dir-
ectly disciplinary either. The ‘‘modulating’’ controls
described briefly but evocatively by Gilles Deleuze
(1992) are more in view here than direct concern with
discipline or with the behavior of individuals. Ruppert
argues that databases work with an ontology of sub-
jects that creates profiles – data bodies or data doubles
– based on their activities, connections, performances,
transactions, and movements that relate to govern-
ment. These data ‘‘make up’’ the people in the system
purview, in ways that are constantly shifting, fluctuat-
ing. In this way, a neo-liberal logic of control fits neatly
with the ways that individuals are ‘‘made up’’ by data.
If the role of ‘‘data doubles’’ in determining the life-
chances and choices of individuals was a major concern
of an earlier phase of surveillance studies (see e.g. Lyon,
2001) then its Big Data magnification will likely inten-
sify such concerns, both analytically and in terms of
critique and political contestation. ‘‘Data doubles’’
becomes a double-entendre.
The kind of ‘‘soft biopower’’ (Cheney-Lippold, 2011:
166) associated with Big Data is at work in marketing
as in parallel ways to those ‘‘harder’’ forms found in
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‘‘national security.’’ Marketing moved from demo-
graphic (Gandy, 1993) to more psychographic cate-
gories in the 1990s and then as marketing went
online, was able to use search histories to create further
consumer clustering, superimposed on the former cate-
gories. Algorithms are used increasingly to target par-
ticular kinds of consumers in relation more to real-time
web use than to the older categories of census and post-
code. This contributes to cybernetic-type control, where
what is assumed to be normal and correct behavior is
embedded in circuits of consumer (or employment,
health, or education) practices. This is also significant
for what Snowden has revealed, as we shall see.
This argument also suggests the need for a shift in
focus from some accounts that refer more directly to
organizations and individuals, to ones that acknow-
ledge – as privacy advocates and others have argued
for some time – that online subjects are also difficult
to define, are not really amenable to the kinds of indi-
vidualist characterizations common in some ‘‘privacy’’
discourses and are hard to connect with the kinds of
actors that might be called upon to raise questions
about Big Data surveillance in the political realm.
There exists, of course, a recognition of privacy in
human rights codes and constitutional documents but
keeping these in view is a constant challenge.
The Deleuzian approach appears to fit the surveil-
lance-analysis bill in some ways, because it acknow-
ledges the shift from just the ‘‘state’’ to other
surveillance agencies, from ‘‘individuals’’ to ‘‘divi-
duals’’ and from discipline to control. As Mattelart
and Vitalis observe, a Deleuzian approach also high-
lights the mobile and invisible nature of much surveil-
lance and the ways that it depends on the involuntary
participation of individuals – metadata again – and the
purpose of anticipating behavior (Mattelart and Vitalis,
2014) that is overwhelmingly evident in the Snowden
disclosures. At the same time, a Deleuzian approach is
misleading if one imagines that the world of top-down
government-based surveillance is a thing of the past.
Such practices now appropriate data from the ‘‘rhi-
zomic’’ forms of surveillance described by Deleuze.
Surveillance in the era of Big Data, then, does not
focus only on the body or on a population but on def-
initions to which we may contribute as part of our daily
online interactions. It ‘‘makes up’’ the data double,
Deleuze’s ‘‘dividual’’ and that entity then acts back
on those with whom the data are associated, informing
us who we are, what we should desire or hope for,
including who we should become. The algorithms grip
us even as they follow us, producing ever more infor-
mation to try to make the user data more effective.
Users discover, one might say, that the price of our
freedom in both political and consumer contexts is
our shaping or conditioning by algorithms.
Anticipation
The political-economic and socio-technical responses to
9/11 helped to change the ‘‘tense’’ of surveillance in
some significant ways (Genosko and Thompson,
2006). Since at least the 1990s, risk-management tech-
niques have increasingly turned towards attempts to
predict and preempt future developments but the antici-
patory approach was racheted up some further notches
as early forms of data analytics were brought into play.
The frequently advertised notion of ‘‘connecting the
dots’’ was predicated exactly on what might be called
anticipatory analytics, where the aim of amassing and
mining data was ‘‘knowledge discovery,’’ of finding
patterns in data that would point a suspicious finger
towards persons and groups whose associations or
communications added up to a ‘‘person of interest’’
profile. In other words, not merely what they might
be but what they might become, was a significant
factor in assigning riskiness from which it was a short
step to suspicion (Kerr and Earle, 2013).
Big Data builds on these already existing modes of
anticipatory surveillance in an attempt to create new
knowledge using the statistical power of large numbers
to help grasp the fragmented details of individual lives.
The anticipatory approach is common across the range
of Big Data applications. Google Now, for example,
uses just this method to draw on a vast concatenation
of relatable data in order to alert specific users to things
that may have great import for them, from warning
them about delayed flights to offering early diagnoses
of flu (Regalado, 2013). Everyone collects and trans-
mits much data, especially using smart phones, but
also through using any digital device. However, what
Lazar calls ‘‘Big Data hubris’’ appears when it is
assumed that Big Data – in this case, based on user
searches for information about flu – can substitute for
rather than supplement conventional modes of analysis.
As it happens, the conventional forms of analysis still
seem to have a high degree of validity compared with
crowd-sourced methods (Butler, 2013). If this is true of
epidemiology how much more care should be taken
with risk analysis relating to (another rather elastic con-
cept) ‘‘terrorism.’’ In this case, as opposed to that of flu,
there is no regular presentation of accurate, identifiable,
and actionable intelligence. The term itself is politi-
cized, it is well-nigh impossible to distinguish between
a violent and non-violent activist, and with so few facts,
correcting for false positives and negatives is both rick-
ety and risky.
The situation is exacerbated by the fact that antici-
patory approaches are less concerned with the overall
picture of a given individual as with ‘‘premediating and
pinpointing potential dangers’’ (de Goede, 2014). The
problem is that profiles may be built and inferences
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made about individuals with privacy regulations and
data protection in place. The conventional links
between data and the individual have become tenuous
and torn. Amoore (2014: 110) asks if privacy rights can
be associated with Deleuze’s ‘‘dividual’’? Much filtering
and analysis is done, as noted above, before identifiable
individuals come in sight. She further suggests that the
harms are therefore to the ‘‘ . . . associational life, to the
potentiality of futures that are as yet unknowable’’ and
to the very possibility of making a political claim (111).
Science-Technology-and-Society approaches are
important for indicating the importance of an onto-
logical approach to the making-up of data subjects
but this by no means should condone complacency
about the ways that subject positions are still imbri-
cated with neo-liberal notions of, for example,
‘‘deserving poor’’ that still characterize some welfare
system use of Big Data practices (Maki, 2011) or the
profiling of ‘‘bad guys’’ (why do intelligence and poli-
cing agencies persist in using such terms?) in anti-ter-
rorism units. As other studies have indicated, such
invidious categories not only persist but are also ampli-
fied as greater reliance is placed on automated (see e.g.
Gandy, 2012) and actuarial (see e.g. Harcourt, 2007)
methods. One has to ask, what do the increasing
flows of those data between different kinds of organiza-
tions mean for the reproduction of social distinctions –
class, gender, ethnicity – and for public accountability
of data processing bodies?
Big Data practices also encourage the use of auto-
mated decision-making and thus downplay the role of
discretion (see also Ruppert). Seen in classic liberal-
legal terms, automated decisions can easily deprive
individuals of their liberty and property, that trigger
in the US the safeguards of the Due Process Clauses
of the Fifth and Fourteenth Amendments. For exam-
ple, computers can terminate individuals’ Medicaid
benefits, impairing a statutorily-granted property inter-
est (see Citron, 2008). Innocent individuals may be
designated as dead-beat parents, resulting in lost prop-
erty, revoked driver’s and professional licenses, and
injury to their reputations. The US federal govern-
ment’s ‘‘No Fly’’ data matching program labels some
individuals as potential terrorists, resulting in the post-
ponement or denial of air travel, both significant
impairments of liberty rights. Automation, suggests
Danielle Citron, will be a driving force in the retreat
from the discretionary model of administrative law.
Nonetheless, due process does mean that citizens or
consumers can push back against such automation
when it precludes or limits understanding or responding
to suspicions, charges, or cut benefits. However, such
an assumption depends on those citizens and con-
sumers knowing what is happening, which Big Data
approaches make very difficult if not impossible.
Adaptation
We noted earlier that many Big Data practices are
common across different platforms. In this section,
however, we indicate that what might under some cir-
cumstances be acceptable for Google may be highly
unacceptable where the NSA is concerned. Google,
after all, holds the contents of much of the visible inter-
net in its data centers and this includes satellite images,
ground level photos of the built environment in a geo-
spatial database indexed to individuals and organiza-
tions. The electronic activities of hundreds of millions
of people, including emails and search requests are also
known to Google.
As Sean Gallagher (2013) observes, what the NSA
does is essentially similar, capturing call metadata and
gaining access to information like that of Google
through systems like Tempora and possibly PRISM.
But the additional factor is that the NSA, using the
Foreign Intelligence Surveillance Act, can follow up
‘‘exceptions’’ with warrants to check on persons of
interest. This has been possible for some time, a fact
first exposed by former AT&T employee Mark Klein in
2005, when he showed how AT&T helped the NSA to
gain access to its own systems through a splitter that
fed into the Intelligence Traffic Analyzer. It was also
shown in 2006 that the NSA used its phone call data-
base for social network analysis and, according to
information from Snowden in 2013, call data collection
of US to foreign numbers is still occurring.
Curiously, solving just such problems of data storage
and analysis have been key to the operations of Google
and Yahoo!, which prompted the NSA to improve on
Google’s BigTable systems with a program called
Accumulo, that has multiple levels of security access.
It can also generate near real-time reports from data
patterns, such as words or addresses from a range of IP
addresses, right across the internet. Through what are
called ‘‘iterators,’’ emergent patterns are constantly
reported back to the NSA so that it can ‘‘visualize’’
links between entities based on relationships and attri-
butes. In this way it resembles Facebook’s social graph,
which is a global mapping system of users and how they
are related to each other; it is the largest social network
dataset in the world. PRISM offers online NSA access
to cloud providers, primarily seeking metadata, which
completes the circle. The NSA’s new data center in
Utah with its huge data-storage capabilities will
enable the expansion of PRISM-type real-time internet
surveillance (although being classified, the precise
purposes are unpublished).
One question for privacy advocates and others is
whether or not these surveillance operations are legal:
they contend that such programs violate laws designed
to protect the liberty and privacy of citizens.
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The assurances given, in the US and other countries,
that citizens are not targeted by these systems, have
failed to reassure citizens and privacy advocates. It is
also clear that some data are ‘‘incidentally’’ collected on
the whereabouts of domestic cellphones. Such data may
be used to map users’ relationships, as noted earlier in
relation to CoTraveler (see Gellman and Soltani, 2013).
It is crucial to distinguish between different kinds of
consequences. As noted above, marketing uses of Big
Data analytics cannot simply be extended to
anti-terrorist pre-emption. Marketers will be satisfied
with results that are accurate only in a relatively small
proportion of cases, just because the cluster around
that group will also be profitable, albeit to a lesser
extent. The economic harms to individuals from such
inaccuracy, though potentially serious, are seldom con-
sidered by marketers (see e.g. Gandy, 2013; Turow,
2012) and are in some contexts fairly inconsequential
(Amazon suggesting some books in which readers have
no interest, for example). But the attempt to find ter-
rorist ‘‘needles’’ in Big Data ‘‘haystacks’’ is fraught
with palpable problems. Such ‘‘needles’’ are, generally
speaking, clever, determined, and imaginative in their
attempts to evade detection. The needle-and-haystack
argument carries with it a high probability of false posi-
tives, which do matter immediately and intensely
because the likelihood is high of harm to specific
persons.
Critique
The question of Big Data, understood in relation to the
Snowden disclosures, has generated unprecedented
public interest in surveillance in many countries
around the world. While technical and legal responses
have been made and while at the level of civil society
much activity is evident, particularly demanding
accountability – and, where appropriate, abolition of
some programs – from the NSA and its cognate agen-
cies, less progress has been made on what might be
called a broad ethical front. Yet the questions raised
are profound ones for which there are no ready answers
and thus, I suggest that an ethical turn becomes more
urgent as a mode of critique. This is so at several levels,
but particularly in the kinds of ways that Snowden him-
self indicates through his repeated questions about
‘‘what kind of society do we want?’’
We began with the question of capacity which is
reflected in the popular metaphors used about Big
Data, notably the handily alliterating ‘‘data deluge.’’
The metaphors associated with Big Data are revealing
for the hopes and fears associated with Big Data. As
Deborah Lupton (2013) has observed, many are asso-
ciated with liquidity. Unlike the metaphors first
adopted for computer technologies, that invoke a
‘‘natural’’ world of the web, cloud, bug, virus, mouse,
and spider, Big Data tropes ‘‘relate to streams, flows,
leaks, rivers, oceans, waves’’ but also to floods or tsu-
namis that may seem to threaten to swamp or drown
us. They are potentially uncontained, out-of-control.
But there is more to the liquidity issue than metaphors
such as the data deluge.
Under the heading ‘‘liquid surveillance’’ I discussed
with Zygmunt Bauman the ways in which data flow
increasingly freely within and between containers and
in particular the ways that digital surveillance has a
seemingly symbiotic relationship with the kind of
liquidity visible in contemporary social, political, and
economic arrangements, that are often short-term, fis-
siparous (Bauman and Lyon, 2013). They also query
the kinds of ‘‘blockages and resistances, the solidities
that may impede the fluid circulation of data’’ (Lupton,
2013) that tend to be omitted from the free-flow-of-data
accounts. The liquidity of surveillance is as signifi-
cant in the social and political realm as at the level of
data-flows.
One theme of Liquid Surveillance is the need for
properly ethical practices. Big Data is currently domi-
nated by commercial and governmental criteria and
these are often met with technical demands (for better
encryption for example) or legal demands (for legisla-
tion relevant to today’s technologies). Privacy advo-
cates and internet activists also try to promote new
political approaches to emergent tendencies such as
Big Data. But a key reason why those commercial
and governmental criteria are so imbricated with Big
Data is the strong affinity between the two, particularly
in relation to surveillance. Big Data represents a con-
fluence of commercial and governmental interests; its
political economy resonates with neo-liberalism.
National security is a business goal as much as a polit-
ical one and there is a revolving door between the two
in the world of surveillance practices (Ball and Snider,
2013).
Properly ethical practices are at a relative disadvan-
tage for several other reasons as well. Not many ethi-
cists spend time thinking about the complexities of the
internet, social media, or Big Data and many of those
at the forefront of the Big Data field seem to have little
time for ethics except as a minor, residual concern (see
Narayanan and Vallor, 2014). The imperatives for Big
Data approaches come from a belief in the immense
power of technology – can Google really track and pre-
dict the spread of flu faster than centers for disease
control? (Ginsberg et al., 2009; Lazer et al., 2014) –
along with the capacity to analyze vast quantities of
data at steadily shrinking unit costs. But just as in the
Google flu example, questions must be asked about
how good are the surveillance data and the modes of
analysis?
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How data are generated and framed always has deci-
sive effects on the final outcomes of analysis. As Lisa
Gitelman reminds us, ‘‘raw data is an oxymoron’’
(2013); data have always been ‘‘cooked’’ as Geoff
Bowker says in the conclusion of Gitelman’s book.
Terms such as metadata, so crucial to Big Data surveil-
lance, lack clear definition, even though it can generally
be distinguished from data such as the content of phone
calls or emails. Yet those ill-defined metadata are used,
constantly, by security and intelligence agencies, and the
patterns revealed by the algorithms used to filter them
relate back to the purposes that shape the data in the first
place and forward to those affected by the designation of
groups that may contain persons of interest.
The range of ethical issues relating to Big Data sur-
veillance is considerable, but from what has been dis-
cussed in the foregoing, may be clustered as privacy,
social sorting, and preemption.
Given the reliance on western liberal legal traditions
it is hardly surprising that public debate generally com-
mences around the question of privacy. Understood as
a human right, it underlies aspects of democratic polity,
such as freedom of expression. Often understood in the
post-Snowden era as relating to control of communica-
tions about oneself, it is clearly a threatened value if not
– according to some – a forlorn hope. Following the
above argument, though, it is vital that an ethics of Big
Data practices be found that deals with the problem of
the increasing gap between data and individuals
(Amoore, 2014; Stoddart, 2014). But as privacy is still
the preeminent mobilizing concept for opposition to
inappropriate, disproportionate or illegal surveillance,
the efforts of those who propose technical limits such as
encryption or de-identification or who would re-infuse
the concept with content appropriate to a Big Data
world are certainly welcome.
As far as social sorting is concerned, this is a concept
that alerts us to several related practices that produce
uneven and unequal outcomes when the supposedly
neutral and illuminating techniques of Big Data –
especially predictive profiling – are applied to perceived
social and political problems. This connects surveil-
lance both with modern bureaucratic practices and
also, under the sign of security, with insurance logics
that see security as procurable through intelligence
gathering, identification, and tracking (Lyon, 2007;
Zedner, 2009). Its outcomes – amplified in Big Data
contexts – are above all the growth of categorical sus-
picion (the parallel in consumer surveillance, is what I
term ‘‘categorical seduction’’ Lyon, 2007). This in turn
encourages a consequentialism that departs from earlier
notions of proportionate punishment to deterrence
and incapacitation. Together with a ‘‘penal populism’’
that calls for public protection, reinforced by media-
enhanced perceptions of risk, time-honored
commitments to the presumption of innocence, or
proof beyond reasonable doubt are eroded (Zedner,
2009: 80).
Thirdly, an emphasis on preemption takes the actu-
arial logic one stage further, connecting with what was
said above about how Big Data fosters an anticipatory,
future tense approach to surveillance. Again this is not
a new development in surveillance. Risk-management
in particular has encouraged such anticipatory govern-
ance for several decades. But the availability of Big
Data techniques encourages an intensified future-orien-
tation in practice. So the possibility that, because of
certain data fragments, the data-body may be thought
to have a propensity to certain behaviors that are not
yet evident, leads to some action. The data have effects;
they are, as Rita Raley says, ‘‘performative.’’ Following
Haggerty and Ericson’s (2000) Deleuzian discussion of
the surveillant assemblage, Raley points out that,
the composition of flecks and bits of data into a profile
of a terror suspect, the re-grounding of abstract data in
the targeting of an actual life, will have the effect of
producing that life, that body, as a terror suspect.
(Raley, 2013: 128)
Conclusion
The main question addressed in this article is in two
parts: One, in what ways and to what extent do the
Snowden disclosures indicate that Big Data practices
are becoming increasingly important to surveillance?
The answer, clearly, is yes, they are. Many of the
major Snowden revelations, especially those in which
metadata feature prominently, indicate a reliance upon
Big Data practices. The second question, following on
from the first, is how far does this indicate changes in the
politics and practices of surveillance? Are new trends, or
the augmentation of older ones, visible here? Again, the
evidence discussed here suggests strongly that Big Data
practices are skewing surveillance even more towards a
reliance on technological ‘‘solutions,’’ and that this
both privileges organizations, large and small, whether
public or private, reinforces the shift in emphasis
towards control rather than discipline and relies increas-
ingly on predictive analytics to anticipate and preempt.
These questions were explored in respect to the capa-
cities of Big Data, their social-political consequences
and the kinds of critique that may be appropriate for
assessing and responding to these developments. For
the first, I argue that ‘‘size’’ is not directly the issue
but rather that, taken together, the loose cluster of
attributes of ‘‘Big Data’’ make a difference in ways
that are hard to generalize. Big Data practices echo
several key surveillance trends but in several respects
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they point to realities that have perhaps been under-
estimated. One is that, within surveillance studies
there has been a general tendency to analyze multiple
forms of surveillance that are not directly linked with
state-based, top-down surveillance of the kind epito-
mized in George Orwell’s Nineteen-Eighty-Four. If
this was understood by some to mean that more gen-
eralized – or, following Gilles Deleuze, ‘‘rhizomic’’ –
surveillance spells less state surveillance activity, the
Snowden revelations are rapidly dispelling that illusion.
However, those revelations, which as I show above,
indicate an increasing dependence on Big Data prac-
tices, also lay bare in ways that were known only
hazily before just how far security and intelligence
agencies depend on data obtained from the commercial
realm. These are consequences that cry out for careful
consideration. In a sense, this means that Orwell’s bleak
vision of what tendencies in post-war liberal democratic
polities could lead to authoritarian surveillance regimes
were not mistaken so much as standing in need of com-
plementary analyses, such as that of his contemporary,
Aldous Huxley, in Brave New World. Big Data prac-
tices in consumer surveillance are (now literally!)
co-travelers with those of state surveillance and
together produce the kinds of outcomes around which
ethical debates should now revolve. Indeed, not only
are they ‘‘co-travelers,’’ they also cooperate extensively,
the one taking methods from the other, with, as dis-
cussed above, potentially pernicious results as the ‘‘suc-
cessful’’ methods in one area are applied in ways
deleterious of human rights in another. Sadly, little
time seems to be spent on such matters in typical com-
puting studies departments in today’s universities,
where all too often notions like privacy and civil liber-
ties are regarded as a nuisance that slows research
development (Narayanan and Vallor, 2014).
It is these matters in particular that attract critique,
especially in relation to anticipatory and preemptive
approaches common to Big Data mindsets and activ-
ities and amplifying what is a long-term surveillance
trend. These fit neatly, of course, with currently inten-
sifying political styles of neo-liberalism that, with
regard to ‘‘national security,’’ are seen in a list towards
actuarialism and a consequentialist concern with mana-
ging disorder and crime rather than seeking its causes
and attempting to eradicate them (Agamben, 2013). Let
me give two examples. Critically, certain time-honored
legal protections such as a presumption of innocence or
proof beyond reasonable doubt are being eroded within
a number of western societies precisely due to the
developing reliance on big-data-led beliefs that suspects
can be isolated by category and algorithm. Even if one-
time ‘‘suspects’’ have their names cleared by judicial
process, the fact that Big Data practices exemplified
in the collect-it-all slogan include retaining data
indefinitely, it can be hard for persons with a
‘‘record’’ ever to make a fresh start. Data in the
Canadian Police Information Centre, for example,
remain there permanently. And when police include
mental health problems in their records these can lead
to denial of entry to Canadians trying to cross the
border into the US. Attempted suicide calls, for exam-
ple, have been uploaded to international databases with
just this outcome (CBC, 2014).
Snowden’s revelations have done good service in
showing how far state-based surveillance extends but
also how much it depends on Big Data practices that
implicate corporate bodies and connect directly with
everyday practices of ordinary internet and cellphone
users. Ethically, he frequently, and wisely, asks what
kind of society we want to live in. Is it one marked
by fear and mutual suspicion, where data are collected
promiscuously and kept forever, in systems that never
forget, making forgiveness obsolete and creating much
to fear even though you have nothing to hide? Is it one
where vulnerability is amplified, democracy diminished
and where ordinary people are more exposed to organ-
izations that are themselves more opaque? These are
questions that Big Data surveillance obliges us to
confront.
Acknowledgement
The author wishes to thank Chris Parsons and Ian Kerr and
also two anonymous reviewers for their constructive critique.
Declaration of conflicting interest
The author declares that there is no conflict of interest.
Funding
This research received no specific grant from any funding
agency in the public, commercial, or not-for-profit sectors.
Notes
1. https://www.schneier.com/blog/archives/2013/09/
metadata_equals.html/
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