data perturbation
Data perturbation is the process of intentionally adding noise or making small changes to a dataset in order to protect the privacy of individuals or to improve the robustness of machine learning models. By introducing randomness to the data, sensitive information can be obscured without significantly altering the overall patterns or trends. This technique is commonly used in data privacy research and in training more generalizable models.
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Related Concepts (1)
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