Lone Gunman

Jef­frey Rosen, law pro­fes­sor at George Wash­ing­ton Uni­ver­sity (GWU), has called the cur­rent incar­na­tion of the Inter­net “a dig­i­tal world that never for­gets” in a recent piece on pri­vacy for the The New York Times.

It’s an astute arti­cle look­ing at the idea of seg­mented iden­ti­ties, the search for a way to safely con­trol our online iden­ti­ties, and some inter­est­ing spec­u­la­tion on dig­i­tal rep­u­ta­tions and their pos­si­ble impor­tance in the future.

Of par­tic­u­lar inter­est to me are two stud­ies Rosen weaves into his story on how pri­vacy on the Inter­net influ­ences our lives and how we can be nudged to become more pri­vacy aware:

Accord­ing to a recent sur­vey by Microsoft, 75 per­cent of U.S. recruiters and human-resource pro­fes­sion­als report that their com­pa­nies require them to do online research about can­di­dates, and many use a range of sites when scru­ti­niz­ing appli­cants — includ­ing search engines, social-networking sites, photo– and video-sharing sites, per­sonal Web sites and blogs, Twit­ter and online-gaming sites. Sev­enty per­cent of U.S. recruiters report that they have rejected can­di­dates because of infor­ma­tion found online, like pho­tos and discussion-board con­ver­sa­tions and mem­ber­ship in con­tro­ver­sial groups.

and:

Accord­ing to M. Ryan Calo, who runs the consumer-privacy project at Stan­ford Law School, exper­i­menters study­ing strate­gies of “vis­ceral notice” have found that when peo­ple nav­i­gate a Web site in the pres­ence of a human-looking online char­ac­ter who seems to be actively fol­low­ing the cur­sor, they dis­close less per­sonal infor­ma­tion than peo­ple who browse with no char­ac­ter or one who appears not to be pay­ing attention.

via @finiteattention

Mar­ket seg­men­ta­tion is a method of group­ing peo­ple with sim­i­lar char­ac­ter­is­tics, pri­mar­ily for mar­ket­ing purposes.

A num­ber of years ago, USA Today described in detail the infor­ma­tion large con­sumer seg­men­ta­tion busi­nesses track and use to group us. It’s an eye-opening read:

The [con­sumer seg­men­ta­tion busi­nesses] are pin­point­ing who lives where; what they’re most likely to read, drive and eat; how many kids they have; and where they shop. And they are doing it with unprece­dented pre­ci­sion. They are going far beyond the char­ac­ter­is­tics of peo­ple in cer­tain ZIP codes to details about peo­ple in spe­cific neigh­bor­hoods — even indi­vid­ual households. […]

Most of the infor­ma­tion they gather is pub­lic: the Cen­sus and gov­ern­ment records of births, deaths, mar­riages, divorces, prop­erty deeds, tax rolls and car reg­is­tra­tions. What’s not pub­lic, peo­ple give away. They do it every time they fill out a war­ranty card, answer a sur­vey, buy a car or use their fre­quent shopper’s cards at drug­stores and supermarkets.

The arti­cle notes that there were/are five com­pa­nies that offer this ser­vice to businesses, and I decided to look fur­ther at the ser­vice offered by the old­est of these com­pa­nies: the 30 year-old Nielsen Clar­i­tas PRIZM NE system.

The sys­tem is fas­ci­nat­ingly crafted, split­ting indi­vid­ual U.S. house­holds into 66 demo­graph­i­cally and behav­iorally dis­tinct ‘seg­ments’. Each of these seg­ments con­tain infor­ma­tion on a member’s likely: age range, edu­ca­tion level, race, home­own­er­ship sta­tus, employ­ment sta­tus (and job type) and their typ­i­cal lifestyle pref­er­ences (e.g. likely travel des­ti­na­tions, favourite shops, typ­i­cal hob­bies, likely read­ing habits, etc.). These 66 seg­ments are then fur­ther seg­mented into one of 14 broader social groups by tak­ing into con­sid­er­a­tion their afflu­ence and loca­tion (i.e. urban, sub­ur­ban, sec­ond city and town and rural).

These two doc­u­ments I man­aged to find are def­i­nitely worth flick­ing through if you’re interested:

• PRIZM NE Seg­ments: Lists the 66 seg­ments and their social group­ings, with the attrib­utes men­tioned above (two pages pdf).
• PRIZM NE Seg­ment Nar­ra­tives: Describes the seg­ments and social group­ings in a nar­ra­tive form (46 pages, pdf)

Data col­lec­tion and min­ing can be quite lucra­tive pur­suits for many retail­ers, and tech­no­log­i­cal advances are pro­vid­ing them with more novel and exten­sive meth­ods of doing just that.

Data min­ing is a topic I’ve been fas­ci­nated with ever since I was intro­duced to it in uni­ver­sity, and this look at how dig­i­tal coupons track us and pro­vide retail­ers with detailed data is a wor­thy addi­tion to my vir­tual collection:

Invented over a cen­tury ago as anony­mous pieces of paper that could be traded for dis­counts, coupons have evolved into track­ing devices for com­pa­nies that want to learn more about the habits of their customers. […]

Many of today’s dig­i­tal ver­sions use spe­cial bar codes that are packed with infor­ma­tion about the life of the coupon: the dates and times it was obtained, viewed and, ulti­mately, redeemed; the store where it was used; per­haps even the search terms typed to find it.

A grow­ing num­ber of retail­ers are mar­ry­ing this data with infor­ma­tion dis­cov­ered online and off, such as guesses about your age, sex and income, your buy­ing his­tory, what Web sites you’ve vis­ited, and your cur­rent loca­tion or geo­graphic rou­tine — cre­at­ing pro­files of cus­tomers that are more detailed than ever, accord­ing to mar­ket­ing companies. […]

Many com­pa­nies have the tech­nol­ogy — and cus­tomers’ per­mis­sion, thanks to the pri­vacy poli­cies that users accept rou­tinely with­out read­ing — to track minute details of people’s movements.

I’m mostly fine with this sort of track­ing as it is typ­i­cally done on a large, imper­sonal level: com­plex algo­rithms are used to deter­mine when to send what vouch­ers to who, all with­out direct human inter­ven­tion. The piece ends with a thought that is some­what close to my opin­ion on this par­tic­u­lar pri­vacy debate: “I would be con­cerned […] if they get very gran­u­lar and are track­ing me specifically.”

via @Foomandoonian

Twelve years after the sign­ing of the Belfast ‘Good Fri­day’ Agree­ment sig­nalled an end to the Trou­bles, North­ern Ire­land is in a state of ‘seg­re­gated peace’, says Kevin Cullen, describ­ing the sit­u­a­tion.

Not only is there an offi­cial ethos of sep­a­rate but equal, but an infra­struc­ture under­pin­ning it. There are three times as many so-called peace lines — elab­o­rate walls sep­a­rat­ing working-class neigh­bor­hoods — than there were at the height of the Trou­bles, 88 of them at last count. […]

With seg­re­ga­tion the sta­tus quo, there is an enor­mous dupli­ca­tion of pub­lic ser­vices, such as schools, com­mu­nity cen­ters, and health clin­ics. The Alliance Party […] esti­mates that dupli­ca­tion of pub­lic ser­vices costs more than $1 bil­lion a year, this in a place the size of Con­necti­cut with a pop­u­la­tion of less than 2 million.

But it’s more than money that North­ern Ire­land is los­ing. It is los­ing the very kind of peo­ple that might change things. Some are vot­ing with their feet, oth­ers sim­ply not vot­ing at all. Vot­ing par­tic­i­pa­tion, which surged in the opti­mism fol­low­ing the Good Fri­day Agree­ment, has slumped. The brain drain, which saw edu­cated young peo­ple head to Eng­land and every­where else, slowed after every­thing looked pos­si­ble in 1998. But it has picked up again, as a new gen­er­a­tion that grew up with­out wide­spread vio­lence con­cludes that peace is nice but not every­thing. So much cre­ativ­ity, energy, and pro­duc­tiv­ity, lost across the Irish Sea.

via Link Banana

That CCTV doesn’t sub­stan­tially help in reduc­ing crime has been shown beyond rea­son­able doubt, pro­poses Bruce Schneier, so now the press­ing ques­tion is whether or not the ben­e­fits secu­rity cam­eras do afford are worth­while.

There are excep­tions, of course, and pro­po­nents of cam­eras can always cherry-pick exam­ples to bol­ster their argu­ment. These suc­cess sto­ries are what con­vince us; our brains are wired to respond more strongly to anec­dotes than to data. But the data are clear: CCTV cam­eras have min­i­mal value in the fight against crime. […]

The impor­tant ques­tion isn’t whether cam­eras solve past crime or deter future crime; it’s whether they’re a good use of resources. They’re expen­sive, both in money and in their Orwellian effects on pri­vacy and civil lib­er­ties. Their inevitable mis­use is another cost. […] Though we might be will­ing to accept these down­sides for a real increase in secu­rity, cam­eras don’t pro­vide that.

In August 2009 Schneier dis­cussed a report that showed only one crime per thou­sand cam­eras per year is solved because of CCTV and quotes David Davis MP say­ing that “CCTV leads to mas­sive expense and min­i­mum effec­tive­ness. It cre­ates a huge intru­sion on pri­vacy, yet pro­vides lit­tle or no improve­ment in security.”

A Home Office study also con­cluded that cam­eras had done “vir­tu­ally noth­ing” to cut crime (although they were effec­tive in pre­vent­ing vehi­cle crimes in car parks), but do “help com­mu­ni­ties feel safer” (a case of clas­sic secu­rity the­atre).