International Business Machines Corporation

Wael Shama

Jonathan Bnayahu

Artem Barger

Eliezer Segev Wasserkrug

In an approach for computing trade-offs between privacy and accuracy of data analysis on building a learning model, a processor receives a dataset for training a model. The dataset includes one or more pre-identified sensitive data fields. The processor determines a weight of each sensitive data field for the model. The processor evaluates resource cost of applying a privacy preservation technique to the one or more pre-identified sensitive data fields. The processor identifies correlation among the sensitive data fields. The processor presents a comparison of options for training the model, in terms of tradeoffs of accuracy for training the model and the resource cost of the privacy preservation technique.

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Nof Hagalil, Israel

Wael Shama