It’s a fiscal touchstone of the Internet era: there’s enormous value to be found in the information we collectively share through our online likes, check-ins, searches, browsing and buying histories.
Companies of all kinds are keen to learn ever more about us, and are increasingly willing to pay for the privilege, notes Bernardo Huberman, HP Senior Fellow and director of the Social Computing Research Group at HP Labs. Yet currently the people actually generating the data aren’t benefitting financially at all (even though, in many cases, they do receive free access to a useful
“There’s no reason, in principal, why individuals shouldn’t be paid in return for the data they create,” suggests Huberman. “If we can do that while taking into account the privacy attitudes of the participants, we can help people better control how their data is used and at the same time open up new possibilities for innovative social and technological research.”
That’s the argument behind a new paper, “A Market for Unbiased Private Data: Paying Individuals According to their Privacy Attitudes,” [pdf] written by Huberman and HP colleague Christina Aperjis.
For such a market to work, they argue, buyers need to be sure they’re receiving unbiased data and individual sellers must be confident they’re getting the best possible price for their information within their tolerance for sharing.
Existing research shows that people tend to want either a significant price for data they feel is revealing, or very little for data that’s liable to biases of various kinds. The result: buyers generally can’t afford the data sets that would be most useful to them.
What’s your appetite for risk?
The solution, say Aperjis and Huberman, is to create a market that lets sellers participate according to their specific attitudes about privacy and risk.
This results in smaller but statistically valid sets of data that can be made available to buyers. Because of the validity of that data, buyers need to purchase only a fraction of a larger data set to compute reliable statistics about that larger set – allowing them to pay relatively fewer individuals the higher sums it takes to get more valid data.
Trust, transparency, and choice as competitive advantages
Managing the process is an intermediary known as the market-maker. Taking a small cut of every trade, market-makers want to maximize market volume and thus have an incentive to act as an honest broker, enforcing transparency and choice with respect to privacy.
In fact, the better job the market-maker does to promote and protect individuals’ privacy, the more trust will exist within the market – and trust is a critical factor in consumer adoption of new business models. For example, giving individuals the most choice about how and why their data is used can widen the appeal of opting-in, increasing the efficiency of the market and giving the market-maker a competitive advantage.
How to move beyond present-day data markets
Third party dealers of private data sets already exist, note Aperjis and Huberman. What’s been missing is the chance for individuals to participate in these transactions – whether by defining how the data may be used, or to get paid. In particular, the inability to compensate sellers is in no small part due to the fact that prior models for private data markets don’t specify how to set appropriate prices.
For example, simply asking sellers to name a specific dollar value would result in biased datasets because people who value their private information the least would always offer the lowest prices. Instead, Aperjis and Huberman set prices by asking sellers to make a series of choices between payment plans that trade off risk and reward.
That said, the researchers acknowledge that in many common scenarios, individuals do receive a service in return for sharing their private data – a free query from a search engine, for example, or a valuable connection with friends through a social network. But it’s not hard to imagine new markets where buyers would be willing to pay individuals directly for their personal information.
A pharmaceutical company, for example, might need a reliable sample of people with a particular disease and who use a specific drug. In a case like that, say the HP researchers, “it is of the utmost importance for the buyer of the data to obtain an unbiased sample of individuals with certain characteristics.”
The research value of unbiased, private data
Such data sets may cost more money to compile, but that actually makes them more likely to be available for peer review and verification. And that’s a positive change, suggests Huberman.
In a letter published in the Feb 16th edition of Nature, Huberman drew attention to the research value of information produced by users of social media. Yet analyses of such data, he wrote, are typically “not accessible to researchers beyond the authors of the work.”
“In some cases the source of the data itself remains hidden, leading not only to problems of verification but also about the generality of the results,” he added.
In contrast, Huberman and Aperjis’ new approach could create data sets that are both reliable and available to others to either confirm or challenge the research inferences drawn from them.
These sets could be used for commercial purposes, certainly. But they might also drive new insights across many fields of research – in education, for example, in urban planning or for medical applications such as the tracking of disease outbreaks.
“Overall, in a global economy where the proper handling of data is an increasing concern, there’s a lot to be said for creating markets where private data is traded openly,” Huberman suggests, “and for doing it in a way that benefits the people who actually generated it.”