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Princeton's Arvind Narayanan and Ed Felten have published a stinging rebuttal, pointing out the massive holes in Cavoukian and Castro's arguments -- cherry picking studies, improperly generalizing, ignoring the existence of multiple re-identification techniques, and so on.

Cavoukian and Castro are rightly excited by Big Data and the new ways that scientists are discovering to make use of data collected for one purpose in the service of another. But they do not admit that the same theoretical advances that unlock new meaning in big datasets also unlock new ways of re-identifying the people whose data is collected in the set.

Re-identification is part of the Big Data revolution: among the new meanings we are learning to extract from huge corpuses of data is the identity of the people in that dataset. And since we're commodifying and sharing these huge datasets, they will still be around in ten, twenty and fifty years, when those same Big Data advancements open up new ways of re-identifying -- and harming -- their subjects.

Data crunching has grown so sophisticated and powerful, privacy researchers now warn that tracing identities from a pool of supposedly "anonymized" data is not just a possibility, it's a certainty.

"It is depressingly hard to try to anonymize data in a way that resists identification by a committed adversary," Arvind Narayanan, a privacy researcher at Princeton University, told NBC News.

such as the DuckDuckGo search engine, whose motto is "We don't track you" — gain popularity, an incentive would arise.

So far, many companies have tried to dispel such fears by noting that the data they gather, store and analyze remains "anonymous." But that, as it turns out, is not entirely accurate, in that it sells the power of data analysis radically short. Take the analysis of anonymous movement profiles, for example. According to a current study by the online journal Scientific Reports, our mobility patterns are so different that that they can be used to "uniquely identify 95 percent of the individuals." The more data is in circulation and available for analysis, the more likely it is that anonymity becomes "algorithmically impossible," says Princeton computer scientist Arvind Narayanan. In his blog, Narayanan writes that only 33 bits of information are sufficient to identify a person.

A study by New York advertising agency Ogilvy One concludes that 75 percent of respondents don't want companies to store their personal data, while almost 90 percent were opposed to companies tracking their surfing behavior on the Internet.

Is it truly desirable for cultural assets like TV series or music albums to be tailored to our predicted tastes by means of data-driven analyses? What happens to creativity, intuition and the element of surprise in this totally calculated world?

This isn’t the first time this has happened, that big data sets full of personal information – supposedly obscured, or de-identified, as the process is called – have been reverse engineered to reveal some or even all of the identities contained within. It makes you wonder: Is there really such a thing as a truly anonymous data set in the age of big data?

That might sound like a bore, but think about it this way: there’s more than taxi cab data at stake here. Pretty much everything you do on the Internet these days is a potential data set. And data has value. The posts you like on Facebook, your spending habits as tracked by Mint, the searches you make on Google – the argument goes that the social, economic and academic potential of sharing these immensely detailed so-called “high dimensional” data sets with third parties is too great to ignore.

University of Colorado Law School associate professor Paul Ohm’s 2009 paper on the topic made the bold claim that “data can be either useful or perfectly anonymous but never both.”

but we do know that third-party data brokers sell all manner of information to businesses, including “police officers’ home addresses, rape sufferers, and genetic disease sufferers” as well as suspected alcoholics and cancer and HIV/AIDS patients.

The first is that almost everything is personal. In the words of computer scientists Arvind Narayanan (Princeton

this model simply doesn’t reflect the reality of the deeply unequal situation we now face. Those who wield the tools of data tracking and analytics have far more power than those who don’t.