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Following is an article from the New York Times of March 16, 2010.

\subsection*{How Privacy Vanishes Online}

\noindent By STEVE LOHR

If a stranger came up to you on the street, would you give him your
name, Social Security number and e-mail address?

Probably not.

Yet people often dole out all kinds of personal information on the
Internet that allows such identifying data to be deduced.  Services
like Facebook, Twitter and Flickr are oceans of personal minutiae ---
birthday greetings sent and received, school and work gossip, photos
of family vacations, and movies watched.

Computer scientists and policy experts say that such seemingly
innocuous bits of self-revelation can increasingly be collected and
reassembled by computers to help create a picture of a person's
identity, sometimes down to the Social Security number.

``Technology has rendered the conventional definition of personally
identifiable information obsolete'', said Maneesha Mithal, associate
director of the Federal Trade Commission's privacy division.  ``You
can find out who an individual is without it''.

In a class project at the Massachusetts Institute of Technology that
received some attention last year, Carter Jernigan and Behram Mistree
analyzed more than 4,000 Facebook profiles of students, including
links to friends who said they were gay.  The pair was able to
predict, with 78 percent accuracy, whether a profile belonged to a gay
male.

So far, this type of powerful data mining, which relies on
sophisticated statistical correlations, is mostly in the realm of
university researchers, not identity thieves and marketers.

But the F.T.C.\ is worried that rules to protect privacy have not kept
up with technology.  The agency is convening on Wednesday the third of
three workshops on the issue.

Its concerns are hardly far-fetched.  Last fall, Netflix awarded \$1
million to a team of statisticians and computer scientists who won a
three-year contest to analyze the movie rental history of 500,000
subscribers and improve the predictive accuracy of Netflix's
recommendation software by at least 10 percent.

On Friday, Netflix said that it was shelving plans for a second
contest --- bowing to privacy concerns raised by the F.T.C.\ and a
private litigant.  In 2008, a pair of researchers at the University of
Texas showed that the customer data released for that first contest,
despite being stripped of names and other direct identifying
information, could often be ``de-anonymized'' by statistically
analyzing an individual's distinctive pattern of movie ratings and
recommendations.

In social networks, people can increase their defenses against
identification by adopting tight privacy controls on information in
personal profiles.  Yet an individual's actions, researchers say, are
rarely enough to protect privacy in the interconnected world of the
Internet.

You may not disclose personal information, but your online friends and
colleagues may do it for you, referring to your school or employer,
gender, location and interests.  Patterns of social communication,
researchers say, are revealing.

``Personal privacy is no longer an individual thing'', said Harold
Abelson, the computer science professor at M.I.T.  ``In today's online
world, what your mother told you is true, only more so: people really
can judge you by your friends''.

Collected together, the pool of information about each individual can
form a distinctive ``social signature'', researchers say.

The power of computers to identify people from social patterns alone
was demonstrated last year in a study by the same pair of researchers
that cracked Netflix's anonymous database: Vitaly Shmatikov, an
associate professor of computer science at the University of Texas,
and Arvind Narayanan, now a researcher at Stanford University.

By examining correlations between various online accounts, the
scientists showed that they could identify more than 30 percent of the
users of both Twitter, the microblogging service, and Flickr, an
online photo-sharing service, even though the accounts had been
stripped of identifying information like account names and e-mail
addresses.

``When you link these large data sets together, a small slice of our
behavior and the structure of our social networks can be
identifying'', Mr.\ Shmatikov said.

Even more unnerving to privacy advocates is the work of two
researchers from Carnegie Mellon University.  In a paper published last
year, Alessandro Acquisti and Ralph Gross reported that they could
accurately predict the full, nine-digit Social Security numbers for
8.5 percent of the people born in the United States between 1989 and
2003 --- nearly five million individuals.

Social Security numbers are prized by identity thieves because they
are used both as identifiers and to authenticate banking, credit card
and other transactions.

The Carnegie Mellon researchers used publicly available information
from many sources, including profiles on social networks, to narrow
their search for two pieces of data crucial to identifying people --
birthdates and city or state of birth.

That helped them figure out the first three digits of each Social
Security number, which the government had assigned by location.  The
remaining six digits had been assigned through methods the government
didn't disclose, although they were related to when the person applied
for the number.  The researchers used projections about those
applications as well as other public data, like the Social Security
numbers of dead people, and then ran repeated cycles of statistical
correlation and inference to partly re-engineer the government's
number-assignment system.

To be sure, the work by Mr.\ Acquisti and Mr.\ Gross suggests a
potential, not actual, risk.  But unpublished research by them
explores how criminals could use similar techniques for large-scale
identity-theft schemes.

More generally, privacy advocates worry that the new frontiers of data
collection, brokering and mining, are largely unregulated.  They fear
``online redlining'', where products and services are offered to some
consumers and not others based on statistical inferences and
predictions about individuals and their behavior.

The F.T.C.\ and Congress are weighing steps like tighter industry
requirements and the creation of a ``do not track'' list, similar to
the federal ``do not call'' list, to stop online monitoring.

But Jon Kleinberg, a professor of computer science at Cornell
University who studies social networks, is skeptical that rules will
have much impact.  His advice: ``When you're doing stuff online, you
should behave as if you're doing it in public --- because
increasingly, it is''.


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