I work in the machine learning field where I deal with datasets provided by an industrial partner, and one concern of the project is the confidentiality of the data.
My team is working on a fault detection system using those datasets which includes features or columns, with names as Motor_sectionA_speed, Motor_sectionB_torque, Valve_sectionC_pressure, etc. which are subparts of a big system, and if the context is known, they could be traceable to details of our partner machinery and operation.
For publishing some results two options have appeared regarding naming those features:
- Name features as Feature A, Feature B, Feature C, etc: I have seen this for synthetic datasets, where the focus is to highlight the algorithm where the importance of the feature is in its nature (distribution, range, etc) not its name or meaning.
- Name them as Motor_1, Motor_2, Valve_1: One person stated that from training she/he had, the previous option could be unethical because the meaning of the variables is lost and might be misleading. Instead, names can only be simplified as Motor_1, Motor_2, Valve_3, etc.
Is it the first option considered unethical in all cases? or is this a "depends"/gray zone matter?