Transcript PowerPoint - ucsc.edu) and Media Services

surveillance
fdm 20c introduction to digital media
lecture 11.05.2007
warren sack / film & digital media department / university of california, santa cruz
last time
• non-linear media
outline
• history of and surveillance today
• review of the capture model
• definition of privacy
– private versus public
• civil versus economic
– capture
– efficient connections versus resistances
– on the virtue of inefficiencies
• lessig on monitoring and search
– example: monitoring on the web
– example: search on the web
• gandy on data mining
surveillance
• close watch kept over someone or something
• Etymology: French, from surveiller to watch
over, from sur- + veiller to watch, from Latin
vigilare, from vigil watchful
panopticon (1791)
panopticon (1791)
claude-nicolas ledoux’s
salt plant at arc-et-senans (1779)
salt plant at arc-et-senans (1779)
surveillance as a dream
of the 18th enlightenment
• Michel Foucault: “I would say that Bentham was
the complement of Rousseau. What in fact was
the Rousseauist dream that motivated many of
the revolutionaries? It was the dream of a
transparent society, visible and legible in each
of its parts, the dream of there no longer
existing any zones of darkness, zones
established by the privledges of royal power or
the prerogatives of some corporation.”
– the eye of power, a conversation with jean-pierre
barou and michelle perrot
but...
• “They that can give up essential liberty to obtain a
little temporary safety deserve neither liberty nor
safety.”
– Benjamin Franklin, 1759 Historical Review of Pennsylvania
surveillance today
• some artists and art groups concerned with
surveillance
– see the zkm show, [ctrl] space, 2001, curated by
thomas y. levin
– surveillance camera players
• http://www.notbored.org/the-scp.html
– institute for applied autonomy
• http://www.appliedautonomy.com/isee/info.htm
– julia scher
– steve mann
• http://www.eyetap.org/wearcam/shootingback/
technologies of surveillance
• example: viisage & superbowl XXXV
– the company: www.viisage.com
– the technology: eigenfaces
• white.media.mit.edu/vismod/demos/facerec/basic.html
from surveillance to dataveillance
• dataveillance/spying
– carnavor
– echelon
– total information awareness agency
• now the “terrorism information awareness” project
• name change as of may 21, 2003 to mollify congress’
worries about intrusion of the privacy of u.s. citizens
• headed by convicted felon (former admiral) john poindexter
• http://www.darpa.mil/darpatech2002/presentations/iao_pdf/sl
ides/poindexteriao.pdf
– officially ended in september 2003, but see electronic
frontier foundation’s update:
http://www.eff.org/Privacy/TIA/
warrantless wiretaps
• Soon after the September 11, 2001 attacks U.S.
President George W. Bush issued an executive order
that authorized the National Security Agency (NSA) to
conduct surveillance of certain telephone calls without
obtaining a warrant from the Foreign Intelligence
Surveillance Court (FISC) as stipulated by FISA.
• In the case ACLU v. NSA, Detroit District Court judge
Anna Diggs Taylor ruled on August 17, 2006 that the
program is illegal under FISA as well as
unconstitutional under the First and Fourth
Amendments of the United States Constitution. Her
decision is stayed pending appeal. [Wikipedia]
patriot act and post 9/11
• aclu’s analysis
– see http://www.aclu.org/SafeandFree/SafeandFree.cfm?ID=11813&c=207
• new powers of surveillance, search and seizure
• threat to the first, fourth, fifth, sixth, eighth and
fourteenth amendments of the U.S. Constitution
surveillance model versus
capture model
l
• surveillance model: is built upon visual
metaphors and derives from historical
experiences of secret police surveillance
• capture model: is built upon linguistic metaphors
and takes as its prototype the deliberate
reorganization of industrial work activities to
allow computers to track them [the work
activities] in real time
– agre, p. 740
capture (in comparison with surveillance)
• linguistic metaphors (e.g., grammars of action)
• instrumentation and reorganization of existing
activities
• captured activity is assembled from
standardized “parts” from an institutional setting
• decentralized and hetrogeneous organization
• the driving aims are not necessarily political, but
philosophical/market driven
taylorism, fordism and grammars of action
ford assembly line circa 1925
privacy: a definition
• 1.
– a. the quality or state of being apart from company or
observation
– b. SECLUSION: freedom from unauthorized intrusion
<one's right to privacy>
• 2. archaic : a place of seclusion
• source: Merriam Webster
privacy: a culturally specific definition
• Does the U.S. Bill of Rights define an
individual’s “right to privacy”?
• Not explicitly, but...
– inferrably: e.g., Amendment IV: The right of the
people to be secure in their persons, houses, papers,
and effects, against unreasonable searches and
seizures, shall not be violated, and no Warrants shall
issue, but upon probable cause, supported by Oath
or affirmation, and particularly describing the place to
be searched, and the persons or things to be seized.
– implicitly: e.g., Amendment IX: The enumeration in
the Constitution, of certain rights, shall not be
construed to deny or disparage others retained by
the people.
what’s missing from this picture?
private
public
what are the connections between
the public and the private?
private
public
social
civil society
state
economic sphere
see writings by hegel, arendt, gramsci, etc.
e.g., hegel: “civil society” as the domain of rights and
freedoms guaranteed by the state;
gramsci on the disctinction between civil society and
economic sphere
resistances between private and public
private
public
what divides the private from the public?
what reduces the efficiency of the connections
between private and public?
lessig on the merits of inefficiency
• “I am arguing that a kind of inefficiency should
be built into these emerging technologies — an
inefficiency that makes it harder for these
technologies to be misused. And of course it is
hard to argue that we ought to build in features
of the architecture of cyberspace that will make
it more difficult for government to do its work. It
is hard to argue that less is more.”
– Lessig, p. 19
lessig on inefficiency (continued)
• But though hard, this is not an argument
unknown in the history of constitutional
democracies. Indeed, it is the core of much of
the design of many of the most successful
constitutional democracies — that we build into
such constitutions structures of restraint, that
will check, and limit the efficiency of
government, to protect against the tyranny of
government.
– Lessig, p. 19
gandy on the merits of inefficiency
• ...data mining systems are designed to facilitate
the identification and classification of individuals
into distinct groups or segments. From the
perspective of the commercial firm, and perhaps
for the industry as a whole, we can understand
the use of data mining as a discriminatory
technology in the rational pursuit of profits.
However, as a society organized under different
principles, we have come to the conclusion that
even relatively efficient techniques should be
banned or limited because of what we have
identified as unacceptable social consequences
– Gandy, pp. 11-12
digital media versus computer science
• digital media studies: some architectures (e..g.,
democratic ones) are best designed to be
inefficient
• computer science: efficiency is almost always
considered to be a virtue: efficient architectures
are usually good architectures
lessig on architecture
• however, by “architecture” lessig means, more
or less, what computer scientists mean when
they say architecture:
configuration/assemblages of hardware and
software
lessig on code and architecture
• The code of cyberspace -- whether the Internet, or net within the
Internet -- the code of cyberspace defines that space. It constitutes
that space. And as with any constitution, it builds within itself a set
of values, and possibilities, that governs life there ... I've been
selling the idea that we should assure that our values get
architected into this code. That if this code reflects values, then we
should identify the values that come from our tradition -- privacy,
free speech, anonymity, access -- and insist that this code embrace
them if it is to embrace values at all. Or more specifically still: I've
been arguing that we should look to the structure of our
constitutional tradition, and extract from it the values that are
constituted by it, and carry these values into the world of the
Internet's governance -- whether the governance is through code,
or the governance is through people.
• Open Code and Open Societies: Values of Internet Governance
Larry Lessig (1999)
lessig on architecture of privacy
• Life where less is monitored is a life more
private; and life where less can (legally perhaps)
be searched is also a life more private. Thus
understanding the technologies of these two
different ideas — understanding, as it were,
their architecture — is to understand something
of the privacy that any particular context makes
possible.
– Lessig, p. 1
architectures of privacy
• from doors, windows and fences
• to wires, networks, wireless networks,
databases and search engines
lessig
• monitoring
• search
monitoring on the web
• what does your web browser reveal about you?
• standard HTTP headers:
–
–
–
–
–
–
From: User’s email address
User-Agent: User’s browser software
Referer: Page user cam from by following a link
Authorization: User name and password
Client-IP: Clien’t IP address
Cookie: Server-generated ID label
cookies
• cookies are information that a web server stores
on the machine running a web browser
– try clearing all of the cookies in your web browser
and the visit the www.nytimes.com site
encyption
• symmetric key encryption
• public key encryption
search/elaboration/data mining
•
•
•
•
what lessig calls search,
what agre calls elaboration, and
what gandy discusses as data mining
are converging concerns about the production
of a permanent, inspectible record of one’s nonpublic life and thus a shrinking in size and kind
of one’s private life
searching on the web
• search engines make many things (sometimes
surprisingly) public
agre on “elaboration”
• “The captured activity records, which are in
economic terms among the products of the
reorganized activity, can now be stored,
inspected, audited, merged with other records,
subjected to statistical analysis, ... and so forth.”
– p. 747
“data mining” is one form of
“elaboration”
• gandy (p. 4) on “data mining”:
...data mining is an applied statistical technique.
The goal of any datamining exercise is the
extraction of meaningful intelligence, or
knowledge from the patterns that emerge within
a database after it has been cleaned, sorted
and processed....
goals of data mining
• In general, data mining efforts are directed
toward the generation of rules for the
classification of objects. These objects might be
people who are assigned to particular classes or
categories, such as “that group of folks who
tend to make impulse buys from those displays
near the check out counters at the
supermarket.” The generation of rules may also
be focused on discriminating, or distinguishing
between two related, but meaningfully distinct
classes, such as “those folks who nearly always
use coupons,” and “those who tend to pay full
price.” Gandy, p. 5.
types of data mining
• descriptive: compute a relatively concise,
description of a large data set
• predictive: predict unknown values for a variable
for one or more known variables
– e.g., will this person likely pay their bills on time?
data mining tasks
•
•
•
•
•
regression
classification
clustering
inference of associative rules
inference of sequential patterns
data mining task
• regression: infer a function that relates a known
variable to an unknown variable
– e.g., advertising: how much will sales increase for
every extra $1000 spent on advertising?
data mining task
• classification: given a set of categories and a
datum, put it into the correct category
– e.g., direct-mail marketing: given a person’s zip code,
age, income, etc. predict if they are likely to buy a
new product
data mining task
• clustering: given a data set divide it into groups
– e.g., segmenting customers into markets: given a set
of statistics (e.g., age, income, zip code, buying
habits) about a large number of consumers, divide
them into markets; e.g., “yuppie,” “soccer mom,” etc.
data mining task
• inference of sequential patterns: given a set of
series, determine which things often occur
before others
– e.g., predicting a customer’s next purchase:
determine which products are bought in a series;
e.g., bookstore: intro to spanish 1, intro to spanish 2,
don quixote; e.g., nursery: grass seed, fertilizer, lawn
mower
data mining task
• inference of associative rules: given a set of
sets, determine which subsets commonly occur
together
– e.g., supermarket layout: given a database of items
customers have bought at the same time, determine
which items should adjacent in the store; e.g., if
diapers and milk are often bought with beer, then
place the beer next to the milk.
– e.g., amazon.com’s “people who bought this book
also bought...”
– Amazon’s feature is an example of a “recommender
system” or a “collaborative filter”
data mining applications
• data mining is used for
– market research and other commercial purposes
– science (e.g., genomics research)
– intelligence gathering (e.g., identification of
“suspects” by “homeland security”)
• might data mining be used for the purposes of
less powerful citizens? e.g.,
– news analysis (cf, the function of FAIR)
– government “watch dog” operations (cf., Amnesty
International)
technologies and architectures of privacy
• technologies and architectures are important
influences on the production and change of
private and public space;
• but, they do not independently determine what
is public and what is private (to think they do is
called technological determinism)
• we need to understand not just the machines,
but also the people mediated by these
technologies: we need to understand the whole
as a machination, a heterogeneous network of
people and machines; thus lessig’s mention (in
addition to architecture) of laws, norms, and the
market
architectures and inefficiencies
• sometimes inefficient architectures, inefficient
technologies are good technologies because
they allow for or facilitate resistance by the less
powerful in the face of powerful individuals,
corporations and governments
next time
• social networks / social software