An anonymous reader sends this report from Motherboard: Computer scientists at the University of Pennsylvania have developed an algorithmic framework for conducting targeted surveillance of individuals within social networks while protecting the privacy of untargeted digital bystanders. ... The algorithms are based on a few basic ideas. The first is that every member of a network (a graph) comes with a sequence of bits indicating their membership in a targeted group. If say, the number two bit was set in your personal privacy register, then you might be part of the “terrorist” target population. For an algorithm searching a network for targets, it doesn’t just get to ask to reveal every network member’s bits. It has a budget of sorts, where it can only reveal so many bits and no more. The algorithms work to optimize this scenario such that as many bits-of-interest are revealed as possible. It does this optimization via a notion known as a statistic of proximity (SOP), which is a quantification of how close a given graph node is to a targeted group of nodes. This is what guides the search algorithms.
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