Recent research from Penn State (1) surfaces interesting questions on privacy in the modern world. Privacy has become a stumbling block in the use of valuable data for research and business purposes. Penn State team advocates adding small noise to data to achieve “differential privacy.” Privacy, a theme picked up by regulators with little knowledge of technology, has to be advanced by foundational mathematics. High tech giants, makers of search, faces, operating systems, databases, flashy hardware and next-quarter’s profits, are ill-equipped to solve this problem.
Research has been lagging. Privacy is a mathematical problem and not a data problem. With less than 8 billion samples across the world, it should be relatively easy to assure privacy if its is solved systematically. Regulators, lost in time and space, are attempting to use archaic tools to solve a problem, they deem important. And, big businesses, who want to hoard and abuse data are unlikely to play. Hence, this is a problem, only academics can solve.
Privacy, as important as education and health in the modern context, can only be protected by the application of mathematics. With few distinct samples with limited time horizons, it should not tax academic minds, if they focus on it.
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