surveillance & privacy fdm 20c introduction to digital media lecture 02.03.2005

1. surveillance & privacy fdm 20c introduction to digital media lecture 02.03.2005 warren sack / film & digital media department / university of california, santa cruz

2. last time • donna haraway’s “cyborg manifesto” • identity and cyborg politics • identity and affinity • lisa nakamura’s “identity tourism” • identity and “passing” • identity and difference

3. 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

4. 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

5. panopticon (1791)

6. panopticon (1791)

7. claude-nicolas ledoux’s salt plant at arc-et-senans (1779)

8. salt plant at arc-et-senans (1779)

9. 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

10. 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

11. 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/

12. technologies of surveillance • example: viisage & superbowl XXXV • the company: www.viisage.com • the technology: eigenfaces • white.media.mit.edu/vismod/demos/facerec/basic.html

13. 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/slides/poindexteriao.pdf • officially ended in september 2003, but see electronic frontier foundation’s update: http://www.eff.org/Privacy/TIA/

14. 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

15. 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

16. 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

17. taylorism, fordism and grammars of action ford assembly line circa 1925

18. 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

19. 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.

20. what’s missing from this picture? private public

21. what are the connections between the public and the private? private public state social civil society 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

22. 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?

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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)

29. 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

30. architectures of privacy • from doors, windows and fences • to wires, networks, wireless networks, databases and search engines

31. example: architecture of the web • examples of (anti)monitoring architectural features of the web • HTTP headers • cookies • encryption • example of searching on the web • try “googling” yourself

32. lessig • monitoring • search

33. 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

34. 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

35. encyption • symmetric key encryption • public key encryption

36. 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 non-public life and thus a shrinking in size and kind of one’s private life

37. searching on the web • search engines make many things (sometimes surprisingly) public

38. 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

39. “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....

40. 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.

41. 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?

42. data mining tasks • regression • classification • clustering • inference of associative rules • inference of sequential patterns

43. 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?

44. 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

45. 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.

46. 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

47. 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”

48. 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)

49. 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

50. 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