  Privacy has gained a growing interest nowadays due to the increasing and
unmanageable amount of produced confidential data. Concerns about the
possibility of sharing data with third parties, to gain fruitful insights,
beset enterprise environments; value not only resides in data but also in the
intellectual property of algorithms and models that offer analysis results.
This impasse locks both the availability of high-performance computing
resources in the "as-a-service" paradigm and the exchange of knowledge with the
scientific community in a collaborative view. Privacy-preserving data science
enables the use of private data and algorithms without putting at risk their
privacy. Conventional encryption schemes are not able to work on encrypted data
without decrypting them first. Homomorphic Encryption (HE) is a form of
encryption that allows the computation of encrypted data while preserving the
features and the format of the plaintext. Against the background of interesting
use cases for the Central Bank of Italy, this article focuses on how HE and
data science can be leveraged for the design and development of
privacy-preserving enterprise applications. We propose a survey of main
Homomorphic Encryption techniques and recent advances in the conubium between
data science and HE.
