How The Government Can Search Private Data, And Still Keep It Private

How the Government Can Search Private Data, and Still Keep It Private

Nathan Collins | February 1, 2016

The National Security Agency landed in hot water in recent years for collecting basically everyone's phone records. It justified its actions by saying it needed such information to find terrorists. There are plenty of arguments about why the NSA shouldn't have all that data and power, but new research asks a somewhat more practical question: Given that it already has the data, is there a way to effectively balance privacy and national security? The answer is surprisingly simple — all it takes is injecting a little randomness into the data.

Source: https://www.washingtonpost.com/world/national-security/nsas-bulk-collection-of-americans-phone-records-ends-sunday/2015/11/27/75dc62e2-9546-11e5-a2d6-f57908580b1f_story.html

<more at http://theweek.com/articles/599388/how-government-search-private-data-still-keep-private; related links: http://www.nap.edu/read/19414/chapter/1 (Bulk Collection of Signals Intelligence. Technical Options.  Committee on Responding to Section 5(d) of Presidential Policy Directive 28: The Feasibility of Software to Provide Alternatives to Bulk Signals Intelligence Collection) http://www.pnas.org/content/113/4/913.abstract (Private algorithms for the protected in social network search. Michael Kearnsa, Aaron Roth, Zhiwei Steven Wu, and Grigory Yaroslavtsev. PNAS (Proceedings of the Nationbal Academy of Sciences of the United States of America), vol. 113 no. 4 (January 26, 2016). [Abstract: Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation). The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. These algorithms are natural variants of common graph search methods, and ensure privacy for the protected by the careful injection of noise in the prioritization of potential targets. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets.])>