DOKK Library

Big Data's Other Privacy Problem

Authors James Grimmelmann

License CC-BY-4.0

                                      CHAPTER 1

                                               ■ ■ ■

                                 James Grimmelmann*

                          I. THE MODERN MEMEX
     We still don’t have personal jetpacks or lunar clone colonies, but at
least we got the memex. In 1945, Vannevar Bush, writing with the kind of
foresight usually reserved for mystics and madmen, sketched a design for
the dream desk of the future. Built around a microfilm archive, Bush’s
design lets the user flip through data at will, following associations and
creating new ones. The “intricate web of trails” in the researcher’s brain is
mapped out in the annotations he makes, creating a permanent record of
his discoveries:
      Wholly new forms of encyclopedias will appear, ready made with
      a mesh of associative trails running through them, ready to be
      dropped into the memex and there amplified. The lawyer has at
      his touch the associated opinions and decisions of his whole
      experience, and of the experience of friends and authorities. The
      patent attorney has on call the millions of issued patents, with
      familiar trails to every point of his client’s interest. The physician,
      puzzled by a patient’s reactions, strikes the trail established in
      studying an earlier similar case, and runs rapidly through
      analogous case histories, with side references to the classics for
      the pertinent anatomy and histology. The chemist, struggling
      with the synthesis of an organic compound, has all the chemical
      literature before him in his laboratory, with trails following the
      analogies of compounds, and side trails to their physical and
      chemical behavior.1
Bush called this device the “memex,” but only because he had never seen a
Bloomberg Terminal.
     In a way, any computer with an Internet connection is a memex that
also plays cat videos, but in another (more accurate) way, the Bloomberg
          *      Professor of Law, University of Maryland. This chapter is available for reuse under the
Creative Commons Attribution 4.0 International license,
          1      Vannevar Bush, As We May Think, ATLANTIC MONTHLY, July 1945, at 112, available at

2             PRIVACY RISKS AND PUBLIC BENEFITS OF BIG DATA                                       P T.

Terminal is its true spiritual heir. Despite costing $1,500 a month to lease,
or perhaps in part because of it, the Bloomberg Terminal is the magic sword
that turns mere wheeler-dealers into Masters of the Universe2 who execute
trades with extreme prejudice. Bloomberg users mainline real-time market
data for anything anyone anywhere has ever done a deal in, engage in
warlock-level feats of technical analysis, and enjoy unequalled access to
breaking news, regulatory filings, industry reports, gossip, rumor,
innuendo, propaganda, and everything else that might possibly conceivably
in some imaginable universe affect the price of something worth buying or
selling. The Terminal is absurdly customizable, helping traders track every
last penny, piastre, and paisa of their portfolios. It even has restaurant
reviews and shopping, both optimized for the 1% of the 1% looking to blow
some of the wealth they have just extracted from their fellow man.
     The Bloomberg Terminal, in short, is Big Data’s elite commando strike
force. There have always been quants, with their artisanal small-batch
hand-crafted voodoo finance. But the Bloomberg Terminal makes financial
necromancy accessible to mere mortals. Imagine yourself granted entry to
this Olympus of data, striding here and there, seeing connections, making
associations, tasting the ambrosia of insight. And now imagine, if you will,
that you are being watched.

     It’s not normally news when a crack addict stops coming around to his
dealer. But when the addict is a partner at Goldman Sachs and the crack
is the information flowing from his Bloomberg Terminal, that’s news.3 A
reporter thought to ring up Goldman and inquire: is so-and-so still with the
firm? He hasn’t logged in to Bloomberg lately. It was a nice bit of
journalistic tradecraft, except for one detail: the reporter worked for
Bloomberg News, and knew about the partner’s terminal use because
Bloomberg News employees had access to it. For years, they had been
checking when persons of interest logged in, and what features they
accessed.4 “We were told again and again and again, find ways to use
what’s on the terminal to write stories.”5

          2      Compare TOM WOLFE, THE BONFIRE OF THE VANITIES 11 (1987), with HE-MAN AND THE
MASTERS OF THE UNIVERSE (Filmation syndicated television broadcast 1983–85).
          3      See Mark DeCambre, Terminally Nosy, N.Y. Post, May 10, 2013, at 41; an updated version
is also available at
          4      See Amy Chozick & Ben Protess, Privacy Breach on Bloomberg’s Data Terminals, N.Y.
TIMES, May 10, 2013, at A1, available at
          5      Amy Chozick, Bloomberg Reporters’ Practices Become Crucial Issue for Company, N.Y.
TIMES, June 13, 2013, at A1, available at
C H. 1                 BIG DATA’S OTHER PRIVACY PROBLEM                                              3

     From there, things went pear-shaped in a hurry for Bloomberg.
Goldman’s management called around to understand the extent of the
snooping.6 The rest of the press found out, and wrote about the snooping
with the gleeful ferocity of an athlete who has just discovered the syringes
in his archrival’s locker.7 Regulators from Treasury and the Federal
Reserve asked pointed questions about whether their employees had been
spied on, too.8 Bloomberg cut off its reporters’ access to terminal usage
information, and then, when that failed to stanch the reputational
bleeding, commissioned a nominally “independent” review of its privacy
and security standards.9
     Some have downplayed the privacy implications, pointing out that
Bloomberg reporters could see only general information about users’
activities, not specific searches and stocks.10 They have a point, given the
steady drumbeat of genuinely serious privacy breaches in the news.11 But
even if in this particular case, Bloomberg’s reporters stopped short of the
most dastardly deeds they were technologically capable of, we should not
let their restraint blind us to the full extent of the dastardry Big Data
makes possible.
    We are accustomed to speaking about Big Data’s privacy concerns in
terms of the surveillance it enables of data subjects.12 Anyone high enough
up to take a ten-thousand-foot view can see over fences. Take a wide-angle
shot, zoom and enhance, and you have a telephoto close-up. But consider
          6      See Susanne Craig & Jessica Silver-Greenberg, Hunch About Bloomberg Brought Rivals
Together, N.Y. TIMES, June 1, 2013, at B1, available at
          7      See, e.g., DeCambre, supra note 3.
          8      See Amy Chozick & Ben Protess, More Clients Ask Questions of Bloomberg, N.Y. TIMES,
May 14, 2013, at B1, available at
          9      Samuel J. Palmisano, the former CEO of IBM, who was named to lead the review, is on
the board of the charitable organization run by Bloomberg’s founder, Michael Bloomberg. See Amy
Chozick, Former IBM Chief to Lead Bloomberg Privacy Review, N.Y. TIMES, May 17, 2013, at B6,
available at
privacy-standards.html?smid=pl-share. The review concluded, unsurprisingly to all, that
“Bloomberg has an appropriate Client Data compliance framework in place.” HOGAN LOVELLS AND
          10 See, e.g., William McGeveran, Privacy and the Bloomberg Terminal, CONCURRING

OPINIONS (May 11, 2013),
          11 This is not to say that Bloomberg or its reporters conducted themselves well. For

journalists, data-gathering of this sort is an ethical breach of the first order. They were not engaged
in the clever acquisition of information about people’s activities in other walks of life. No, they
were breaching a trust, using information their own organization solicited for radically different
purposes. If you like, you can regard Bloomberg News as having burned hundreds of sources. Or,
if you prefer, you can treat it as an offense against readers, like putting GPS trackers in every copy
a paperboy delivers. And the corruption—for that is what it was—was truly systemic: hundreds of
reporters took advantage of what they called a feature but most of us would call a bug.
          12 See, e.g., Paul Ohm, Broken Promises of Privacy: Responding to the Surprising Failure of

Anonymization, 57 UCLA L. REV. 1701 (2010).
4                            PRIVACY RISKS AND PUBLIC BENEFITS OF BIG DATA                P T.

now the user of the Bloomberg terminal, zipping from function to function,
running down a hunch and preparing to make a killing. Perhaps he
correlates historical chart data for energy-sector indices with news reports
on international naval incidents in the Pacific Rim. He pulls patterns out
of after-hours trading data, checking them against SEC filings and
earnings calls. He has a theory, about what happens when certain
shipbuilders report their quarterlies—two usually coupled bond funds
briefly diverge—and he stands ready to pocket some cash the next time it
happens by exploiting this informational advantage with overwhelming
financial force. Tell him that someone has been watching every keystroke,
and you will see the blood drain from his face.
   Or for a more sympathetic figure, go back to Bush’s description of a
memex user:
            The owner of the memex, let us say, is interested in the origin and
            properties of the bow and arrow. Specifically he is studying why
            the short Turkish bow was apparently superior to the English long
            bow in the skirmishes of the Crusades. He has dozens of possibly
            pertinent books and articles in his memex. First he runs through
            an encyclopedia, finds an interesting but sketchy article, leaves it
            projected. Next, in a history, he finds another pertinent item, and
            ties the two together. Thus he goes, building a trail of many items.
            Occasionally he inserts a comment of his own, either linking it
            into the main trail or joining it by a side trail to a particular item.
            When it becomes evident that the elastic properties of available
            materials had a great deal to do with the bow, he branches off on
            a side trail which takes him through textbooks on elasticity and
            tables of physical constants. He inserts a page of longhand
            analysis of his own. Thus he builds a trail of his interest through
            the maze of materials available to him.13
For “bow and arrow,” substitute “genital herpes” or “radical politics.” For
the historian, substitute a lawyer on a major case or a journalist on the
City Hall beat. Think about all the people who might find some use in
seeing your browser history research trails: opposing counsel, corrupt
police chiefs, lovers, rivals, frenemies, talk radio demagogues, creepy
bosses, trolls, self-righteous prudes, kooks and zealots of every stripe, and
that one petty-minded neighbor with a lot of grudges and a little time to
kill. The memex is—it is designed to be—an externalized record of its user’s
every thought. Using it is like plugging yourself into the most perfect brain
scanner ever devised. If you care, even just the slightest bit, about your
intellectual freedom, then you, I submit, are very interested in who has

          13 Bush, supra note 1, at 123.
C H. 1                BIG DATA’S OTHER PRIVACY PROBLEM                                            5

access to your memex and the memories it holds.14 Big Data puts the world
at your fingertips—so with Big Data, your fingerprints are everywhere.
     Big Data’s other privacy problem is like its first privacy problem, and
also unlike it. Subject privacy is about actions: Big Data knows what you
did last summer. User privacy is about thoughts: Big Data knows you
watched I Know What You Did Last Summer. With enough such data
points, it can make a pretty good guess what you’re likely to do next
summer. Google searches have been used to convict murderers; how long
before they’re used as evidence of pre-crime?15 Oh, wait, they already are:
the NSA tells analysts to find targets by identifying people “searching the
web for suspicious stuff.”16 Search-query signature strikes cannot be far
     Big Data is recursive: it tends inevitably to convert users into subjects.
How does Google map flu trends? Not by testing people for infection, but
seeing who searches for information about the flu.17 Every visitor to the
Land of Data leaves a little of herself behind. Every query is further grist
for the mill. Even Vannevar Bush, writing in 1945, bless his prescient and
naive heart, understood this much:
     The historian, with a vast chronological account of a people,
     parallels it with a skip trail which stops only on the salient items,
     and can follow at any time contemporary trails which lead him all
     over civilization at a particular epoch. There is a new profession
     of trail blazers, those who find delight in the task of establishing
     useful trails through the enormous mass of the common record.
     The inheritance from the master becomes, not only his additions
     to the world’s record, but for his disciples, the entire scaffolding
     by which they were erected.18
He might have added that we will all be trailblazers, that the greatest
value of the memex—its permanent and globally shared record of every
user’s research trails—is precisely its greatest curse. We are the spiders
spinning Big Data’s web of knowledge, and we are also the flies trapped in
it.19 He who works with data should look to it that he himself does not

DIGITAL AGE (2015)).
          15 See, e.g., Declan McCullagh, Police Blotter: Web Searches Lead to Murder Conviction,

CNET (Feb. 12, 2010),–13578_3–10452471–38.html.
          16 See, e.g., XKEYSCORE 15 (NSA PowerPoint Presentation Feb. 25, 2008), available at
          17, Flu Trends: How Does This Work?,

          18 Bush, supra note 1, at 124.

          19 Another computer visionary, Douglas Engelbart, noted as an aside during his legendary

1968 demo that “an advantage of being online is that it keeps track of who you are and what you’re
doing all the time.” Douglas Engelbart’s Mother of All Demos (Video recording of the presentation
of Douglas Engelbart at the 1968 Fall Joint Computer Conference, Dec. 9, 1968), available at http://
6            PRIVACY RISKS AND PUBLIC BENEFITS OF BIG DATA                                     P T.

become data. And when you gaze long into Big Data, Big Data also gazes
into you.20

     There is another way of understanding the relationship between Big
Data subjects and Big Data users. The fact that users also have privacy
interests at stake complicates the project of protecting subject privacy. To
understand the problem, it helps to understand something of the debate
over what to do about safeguarding those whose personal information has
been hoovered up at terabyte scale.
     For a time, it appeared that no restrictions on use might be necessary
because there were no data subject privacy interests at stake.
Deidentification was the watchword of the day: it was thought that some
simple scrubbing—stripping a dataset of names, ranks, and serial
numbers—would render these data driftnets dolphin-safe. And the
database wranglers would have gotten away with it, too, if it hadn’t been
for those meddling computer scientists.21 Personal information always
contains something unique. It expresses its singularity even in an IP
address, and a very modest grade of data has in it something irreducible,
which is one man’s alone. That something he may be reidentified from,
unless there is a restriction in access to the database. Although there is a
lively dispute about where to draw the balance between the needs of the
many (as data subjects) and the needs of the many (as research
beneficiaries), it is by now painfully clear that some such balance must be
    The next line of defense, implicit in the burgeoning discourse of Big
Data boosterism, is that only incorruptible researchers who are pure of
heart will be plowing through the piles of data in search of ponies.
Epidemiologists are the usual heroes of this story, perhaps because public
health officials would never, ever jump to conclusions about poorly

          20 For another example, consider scientific publisher Elsevier’s purchase of the citation-

management system Mendeley. To its academic users, Mendeley is a mini-memex enabling them
to craft and share associational trails. To Elsevier, those trails are a commodity—or perhaps an
input into a copyright-enforcement system. See, e.g., David Banks, The Mendeley Dilemma,
CYBORGOLOGY (Jan. 22, 2013),
dilemma/; David Dobbs, When the Rebel Alliance Sells Out, THE NEW YORKER (Apr. 12, 2013),
          21 See Ohm, supra note 12 (summarizing reidentification literature).

          22 Compare id. (reidentification = big deal) with Jane Yakowitz, Tragedy of the Data

Commons, 25 HARV.J.L. & TECH. 1 (2011) (reidentification = big whoop). See also Felix T. Wu,
Defining Privacy and Utility in Data Sets, 84 U. COLO. L. REV. 1117 (2013); Daniel Barth-Jones,
The ‘Re-Identification’ of Governor William Weld’s Medical Information: A Critical Re-
Examination of Health Data Identification Risks and Privacy Protections, Then and Now (June 4,
2012), available at
C H. 1                 BIG DATA’S OTHER PRIVACY PROBLEM                                             7

understood diseases sweeping through their communities. This ideal of a
trusted elite priesthood of data analysts bears an uncanny similarity to
National Rifle Association head Wayne LaPierre’s invocation of “good guys
with guns.”23 When Big Data is outlawed, only outlaws will have Big Data.
Actuaries and supply chain optimizers, perhaps, come close to this
technocratic ideal. But Big Data today is probably better embodied by
marketers and hedge-fund traders, two professions not known for their
generous concern for human flourishing. It is hard to feel sanguine about
the Big Swinging Dicks24 who brought us the subprime financial Chernobyl
or about ad men in the business of running A/B tests to optimize their
manipulation of consumers’ cognitive biases.25 Any sufficiently advanced
marketing technology is indistinguishable from blackmail. The global
phishing industry shows what happens when confidence men scale up their
    And all of this is to say nothing about Carnivore, Total Information
Awareness, PRISM, EvilOlive, and the other ominously named trappings
of the National Surveillance State.26 Give the CIA six megabytes of
metadata inadvertently emitted by the most honest of men, and it will find
something in them to put him on the drone kill list. One might—as the
Obama administration asks—simply trust in the good faith and minimal
competence of the Three Letter Agencies that brought us extraordinary
rendition, COINTELPRO, and the Clipper Chip. Or, more realistically, one
might question the wisdom of creating comprehensive fusion centers
accessible to every vindictive cop with a score to settle.27
     Thus, since Big Data cannot be entirely defanged and its users cannot
be entirely trusted, it becomes necessary to watch them at work. It seems
like a natural enough response to the problem of the Panopticon. Subject
privacy is at risk because Big Data users can hide in the shadows as they
train their telescopes not on the stars but on their neighbors. And so we
might say, turn the floodlights around: ensure that there are no dark
corners from which to spy. We would demand audit trails—permanent,
tamper-proof records of every query and computation.
    But if we are serious about user privacy as well as about subject
privacy, transparency is deeply problematic. The audit trails that are

          23 See Wayne LaPierre, Speech to NRA Convention (May 4, 2013), at 9, available at http:// (“No bill in Congress, no Rose Garden speech will
ever change the inescapable fact that the only way to stop a bad guy with a gun is a good guy with
a gun.”).
          24 See Michael Lewis, LIAR’S POKER 56–57 (1989).


          26 See Jack M. Balkin, The Constitution in the National Surveillance State, 93 MINN. L. REV.

1 (2008).
          27 See Danielle Keats Citron & Frank Pasquale, Network Accountability for the Domestic

Intelligence Apparatus, 62 HASTINGS L.J. 1441 (2011).
8            PRIVACY RISKS AND PUBLIC BENEFITS OF BIG DATA                                    P T.

supposed to protect Big Data subjects from abuse are themselves a perfect
vector for abusing Big Data users.28 Indeed, they are doubly sensitive,
because they are likely to contain sensitive information about both subjects
and users. The one-way vision metaphor of the Panopticon, then, is double-
    Think about glasses. A common intuition is that mirrorshades are
creepy because the wearer can see what he chooses without revealing
where his interest lies. Everyone was up in arms about the Google Glass-
holes who wore them into restrooms. But the all-seeing Eye is a window to
the soul. The Segway for your face was also a camera pointed directly at
your brain that synced all its data to the cloud. The assumption Glass users
made, presumably, was that no one else would have access to their data,
and so no one else would be pondering what they’re pondering. But that’s
what Bloomberg Terminal users thought, too.
     This leaves meta-oversight: watching the watchmen. Audit trails don’t
need to be public; access to them could be restricted to a small and
specialized group of auditors. But this privacy epicycle introduces
complications of its own. You have a security problem, so you audit your
users. Now you have two security problems: you are committed to
safeguarding and watching over not just your data, but your data about
how your data is being used. Whoever looks through the logfiles will be able
to gain remarkable insight into users’ methods and madnesses. Yes, the
auditors will be looking for suspicious access patterns, but they’ll need to
have access to the full, sensitive range of information. You wouldn’t want
an insider trading scandal in which an auditor piggybacked on an analyst’s
research, or an auditor who picks a favorite user and turns into a stalker.
Your auditors, in other words, are also Big Data users, which means that
they too will have to be audited. It’s watchmen all the way down.

    This convergence between Big Data’s two privacy problems brings
home the degree to which the Big Data story is a story of centralized
control. It is the accumulation of large repositories of data that makes
comprehensive surveillance possible. When data is lying about raw, in the
wild, any forager can pluck some, but the risks are necessarily limited.
When that same data has been harvested, processed, and warehoused, two
          28 See, e.g., Letter from Dione J. Stearns, Assistant General Counsel, Federal Trade

Commission, to Ginger McCall (Sept. 25, 2012), available at
FTC–Initial–Assessment–09–26–12.pdf (withholding information about Google’s privacy
assessments from release under the Freedom of Information Act in part because detailing how
Google safeguards its users’ secrets would reveal Google’s own secrets). To be sure, Google
requested that the “confidential and proprietary information” be withheld if it was “competitively
sensitive.” But at a finer level of granularity, such a report would include precise and sensitive
details of how Google employees use their access to its massive databases. See generally Yakowitz,
supra note 22, at 17–20 (describing agencies’ use of personal privacy arguments to deny FOIA
C H. 1                  BIG DATA’S OTHER PRIVACY PROBLEM                                                9

things change. First, it becomes far more threatening to the subjects,
precisely because the accumulation of details that are harmless in
themselves can make patterns evident. And second, it becomes necessary
to restrict access to the data: not just anyone can be allowed to run queries
against it with anonymous impunity.
     That is, centralizing data disempowers both subjects and users. They
are both now subject to the policies—or, perhaps, “whims”—of whatever
entity controls the dataset. Data ownership is power, of a peculiarly feudal
ilk. The data barons of Silicon Valley struggle to ensure that data about
people comes to rest in their own servers. Whosoever would work these vast
tracts of data to harvest insight must do the data barons homage. By what
feudal incidents does one become a data vassal? Socage, of course—
payment in coin—but also data service—giving up yet more information
about oneself. The synoptic view that Big Data affords is not, it turns out,
something that will be widely shared. That, the barons will hoard for
     Big Data doesn’t just grow on trees; it isn’t natural or inevitable. It
describes a particular configuration of institutional relationships among
data’s subjects, users, and owners, one in which concatenation and
concentration give a small set of actors disproportionate power to
determine who knows what about whom.29 This is a form of ideology: that
there are invaluable insights available in datasets so large they can only
be effectively managed by centralized repositories. This ideology of Big
Data is explicitly used to justify overriding the privacy concerns of its
subjects,30 and it has the side effect of putting its users in a position where
they are subject to the observation and control of the data barons. Not just
privacy law, but intellectual property law, contract law, unauthorized
access law, and many other bodies of decidedly non-neutral doctrine are
used to create a world in which data is always collected and rarely
distributed.31 This novus ordo datorum serves the interests of autocrats,
bureaucrats, and secret police; it appeals to data-addicted technologists
and venture capitalists on the prowl for their next Internet-scale score.
But—for all the insights that Big Data offers—we can question whether its
arrangements are as good for the rest of us.

          29 See Julie Cohen, What Privacy Is For, 126 HARV. L. REV. 1904, 1918–27 (2013).

          30 See, e.g., Yakowitz, supra note 22, at 66 (calling use of personal data for public-benefiting

research projects “the tax we pay to our public information reserves” and arguing that “if taken to
the extreme, data privacy can make discourse anemic and shallow”).
          31 Subject privacy is, in large measure, a creature of tort law. Tort duties supposedly keep us

safe from GPS trackers and upskirt drones; tort law hangs like the sword of Damocles over Big
Data controllers who guard their super-sized datasets with subpar security (even if that sword
never actually seems to drop). But user privacy is almost entirely a creature of contract law. The
boilerplate contracts that let users inside armor-plated data silos spell out what can, or less often
can’t, be done with the security-camera footage. If they sell your record of searches for panda
gangbangs to the FSB, or your detailed map of flu trends and your interest in viral genetics to the
FBI, they’ll say you have only yourself to blame for clicking where it said “Click here to agree.”
10            PRIVACY RISKS AND PUBLIC BENEFITS OF BIG DATA                                       P T.

    Perhaps there is another way. Consider a different possible ideal for
managing our relationship to information. Call it Small Data, or Local
Data, or Slow Data, or Sustainable Data, or perhaps Democratic Data—
enough to go around, enough for everyone to have some. Not a lot, perhaps,
but enough—enough not to be beholden to anyone else, enough to
participate meaningfully in society, enough that no one can take away your
     What might Democratic Data look like? It might look like citizen
science, in which amateurs around the world participate in the enterprise
of collecting and analyzing datasets, not because they have been tricked
into clicking on the wrong pixel or because the orbital mind-control lasers
are watching their every move—but rather because someone has
persuaded them, honestly and sincerely, that the research project will be
good for humanity.32 It might look like the Welcome Trust, which requires
data analysts to swear that they will contribute what they learn back to
the public.33 It might look like CLOCKSS, the distributed archiving project
whose guiding principle is that anything valuable enough to remember is
too valuable to be left in one pair of hands.34 It might say that big datasets
(small “b” and small “d”) are neither exclusive property nor free-for-all
commons, but the new natural resources, subject to environmental
stewardship for the public good.
     The prevailing ethos of Big Data is paranoid hoarding; data brokers
sit like jealous dragons on their piles of treasure, always hungering for
more. But Democratic Data would treat information like a living thing:
growing, learning, flowering, and eventually fading away when it has
reached the end of its natural span. Big Data is proudly impersonal: it
believes in patterns that abstract away from the contradictory details of
daily life and in insights that defy all human understanding. But
Democratic data is humanist: it believes that knowledge is of the people,
by the people, and above all for the people. Big Data divides subjects and
researchers with a wall of numbers; Democratic Data brings them face to
face. Big Data is literally technocratic; he who wields the tools rules. But
Democratic Data is, yes, democratic. All of us have a say in how it works,
in who uses it for what purposes, and what to do with the knowledge it
    Democratic Data is not the opposite of Big Data: these yeoman
dataholders will join together their datasets at times to serve the public

          32 See, e.g., Katherine Xue, Popular Science, HARV. MAG, Jan-Feb. 2014, at 54, also available

          33 See Wellcome Trust, Policy on Data Management and Sharing, (Aug. 2010), http://www. See also Consent
2.0, THE ECONOMIST Apr. 28, 2013,

C H. 1                BIG DATA’S OTHER PRIVACY PROBLEM                                          11

good, but they will be ever mindful of the risk of data tyranny.35 Alexander
Hamilton would have loved Big Data, with its Enlightenment ambitions,
brutally rational economics, and awe-inspiring centralized power.
Democratic Data is more of a Thomas Jefferson kind of idea—civic,
romantic, uplifting, a little contradictory, and faintly impractical. Perhaps
this might mean giving up the Bloomberg Terminal, or dividing out its
functions and sharing access to some of them more widely. But would we
want to live in a world that sets these ideals aside, rather than seeking,
however fitfully and imperfectly, to realize a Republic of Data, where all
men and women are created equal, and are endowed by their databases
with certain unalienable rights, among them life, liberty, and the pursuit
of privacy?

          35 Cf. Omer Tene & Jules Polonetsky, Big Data for All: Privacy and User Control in the Age

of Analytics, 11 NW. J. TECH. & INTELL. PROP. 239 (2013).