Tag Archives: privacy

Privacy and Identity on the Internet

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 and the PRIZM NE System

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:

Privacy and Tracking with Digital Coupons

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

The CCTV Trade-Off

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).

Identification through Anonymous Social Networking Data

Anonymity is “not sufficient for privacy when dealing with social networks” is the conclusion from a study that has successfully managed to de-anonymise large amounts of sanitised data from Twitter and Flickr.

The main lesson of this paper is that anonymity is not sufficient for privacy when dealing with social networks. […] Our experiments underestimate the extent of the privacy risks of anonymized social networks. The overlap between Twitter and Flickr membership at the time of our data collection was relatively small. […] As social networks grow larger and include a greater fraction of the population along with their relationships, the overlap increases. Therefore, we expect that our algorithm can achieve an even greater re-identification rate on larger networks.

There’s been some meritorious coverage of this study. This from BBC News:

The pair found that one third of those who are on both Flickr and Twitter can be identified from the completely anonymous Twitter graph. This is despite the fact that the overlap of members between the two services is thought to be about 15%.

This from Ars Technica:

It’s not just about Twitter, either. Twitter was a proof of concept, but the idea extends to any sort of social network: phone call records, healthcare records, academic sociological datasets, etc.

via Schneier