The "familiar" privacy remedies that would limit the re-use and sharing of data are inconsistent with the ways in which big data are being used, according to a new paper, "Big Data, Privacy and the Familiar Solutions," by Technology Policy Institute's Thomas Lenard and Paul Rubin. Moreover, there is no evidence that the privacy and security threats often cited by privacy advocates are actually occurring.
In their paper, Lenard, TPI President and Senior Fellow, and Rubin, TPI Senior Fellow and Emory University Professor of Economics, provide numerous examples of the beneficial uses of big data, including tracking health risks, detecting financial fraud, helping underwrite loans to individuals who would otherwise not qualify, and helping consumers find the lowest prices on goods and services. The beneficial uses identified, from both the public and private sector, illustrate "how big data provide the opportunity for significant innovation and value creation."
Lenard and Rubin note that "a standard solution long promoted by privacy advocates is that data should only be collected for a specific, identified purpose." They warn that such policies would be harmful, because "using data in unanticipated ways has been a hallmark of the big data revolution, for commercial, research and even public sector uses."
Lenard and Rubin examine potential privacy and security threats often cited by advocates and find no evidence of the predicted harms. For example, they examine the data on identity fraud and data breaches, finding that there is no evidence that the advent of big data has resulted in an increase in either. In fact, the use of big data could be expected to reduce instances of fraud.
The authors also dispute claims that using data to create predictive models is somehow harmful to consumers and could lead to discrimination. Lenard and Rubin explain that since it uses more data points, "the use of big data should lead to fewer consumers being mis-categorized and less arbitrariness in decision-making." The authors also find no evidence that the use of big data favors the rich over the poor. In fact, price discrimination, or charging different prices to different consumers based on willingness and ability to pay, "should usually work to the advantage of lower-income consumers." The examples of potential discrimination cited in the big data report recently released by the White House are also unconvincing.
"We conclude that there is no evidence at present that big data used for commercial and other non-surveillance purposes have caused privacy harms," state the authors. "Moreover, the standard solutions... would impose barriers to the innovation expected from the big data revolution."
"Big Data, Privacy and the Familiar Solutions" is available on the TPI website. The paper was prepared for the May 14th conference on "The Future of Privacy and Data Security Regulation," hosted by the Law and Economics Center at George Mason University.