  Cryptography and data science research grew exponential with the internet
boom. Legacy encryption techniques force users to make a trade-off between
usability, convenience, and security. Encryption makes valuable data
inaccessible, as it needs to be decrypted each time to perform any operation.
Billions of dollars could be saved, and millions of people could benefit from
cryptography methods that don't compromise between usability, convenience, and
security. Homomorphic encryption is one such paradigm that allows running
arbitrary operations on encrypted data. It enables us to run any sophisticated
machine learning algorithm without access to the underlying raw data. Thus,
homomorphic learning provides the ability to gain insights from sensitive data
that has been neglected due to various governmental and organization privacy
rules.
  In this paper, we trace back the ideas of homomorphic learning formally posed
by Ronald L. Rivest and Len Alderman as "Can we compute upon encrypted data?"
in their 1978 paper. Then we gradually follow the ideas sprouting in the
brilliant minds of Shafi Goldwasser, Kristin Lauter, Dan Bonch, Tomas Sander,
Donald Beaver, and Craig Gentry to address that vital question. It took more
than 30 years of collective effort to finally find the answer "yes" to that
important question.
