'How to quantify privacy, when using homomorphic encryption?
How can you measure how secure or private the new variables are relative to the real (actual) variables.
I want to compare homomorphic encryption and differential privacy in combination with machine learning models. Maybe using a measures like Kullback-Leibler, but I will need the distribution of the encrypted variables.
I'm using the TenSEAL python package and random forest.
Does anyone know a python package or function in TenSEAL to compare the encrypted values with the real values of compute the distribution of the encrypted values?
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