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The volume of "free" data on the internet has been key to the current successof deep learning. However, it also raises privacy concerns about theunauthorized exploitation of personal data for training commercial models. Itis thus crucial to develop methods to prevent unauthorized data exploitation.This paper raises the question: \emph{can data be made unlearnable for deeplearning models?} We present a type of \emph{error-minimizing} noise that canindeed make training examples unlearnable. Error-minimizing noise isintentionally generated to reduce the error of one or more of the trainingexample(s) close to zero, which can trick the model into believing there is"nothing" to learn from these example(s). The noise is restricted to beimperceptible to human eyes, and thus does not affect normal data utility. Weempirically verify the effectiveness of error-minimizing noise in bothsample-wise and class-wise forms. We also demonstrate its flexibility underextensive experimental settings and practicability in a case study of facerecognition. Our work establishes an important first step towards makingpersonal data unexploitable to deep learning models.

Unlearnable Examples: Making Personal Data Unexploitable