An anonymous reader writes: When personal information ends up in the analytical whirlpool of big data, it almost inevitably becomes orphaned from any permissions framework that the discloser granted for its original use; machine learning systems, commercial and otherwise, end up deriving properties and models from the data until the replication, duplication and derivation of that data can never hoped to be controlled or 'called back' by the originator. But researchers now propose a revision which can be imposed upon existing machine-learning frameworks, interposing a 'summation' layer between user data and the learning system, effectively tokenising the information without anonymising it, and providing an auditable path whereby withdrawal of the user information would ripple through all iterations of systems which have utilized it — genuine 'cancellation' of data.
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