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Dataset Evaluation For Data Trading Using Expected Loss And Homomorphic Encryption, Minsung Joo
Dataset Evaluation For Data Trading Using Expected Loss And Homomorphic Encryption, Minsung Joo
Senior Honors Papers / Undergraduate Theses
Supervised machine learning suffers from the ``garbage-in garbage-out" phenomenon where the performance of a model is limited by the quality of the data. While a myriad of data is collected every second, there is no general rigorous method of evaluating the quality of a given dataset. This hinders fair pricing of data in scenarios where a buyer may look to buy data for use with machine learning. In this work, I propose using the expected loss corresponding to a dataset as a measure of its quality, relying on Bayesian methods for uncertainty quantification. Furthermore, I present a secure multi-party computation …