WAHC 2021, 9th Workshop on Encrypted Computing & Applied Homomorphic Cryptography, Associated with the ACM CCS 2021 conference, 15 November 2021, Seoul, South Korea
Fully Homomorphic Encryption (FHE) allows private computation over encrypted data, disclosing neither the inputs, intermediate values nor results. Thanks to recent advances, FHE has become feasible for a wide range of applications, resulting in an explosion of interest in the topic and ground-breaking real-world deployments. Given the increasing presence of FHE beyond the core academic community, there is increasing demand for easier access to FHE for wider audiences. Efficient implementations of FHE schemes are mostly written in high-performance languages like C++, posing a high entry barrier to novice users. We need to bring FHE to the (higher-level) languages and ecosystems non-experts are already familiar with, such as Python, the de-facto standard language of data
science and machine learning. We achieve this through wrapping existing FHE implementations in Python, providing one-click installation and convenience in addition to a significantly higher-level API. In contrast to other similar works, Pyfhel goes beyond merely exposing the underlying API, adding a carefully designed abstraction layer that feels at home in Python. In this paper, we present Pyfhel, introduce its design and usage, and highlight how its unique support for accessing low-level features through a high-level API makes it an ideal teaching tool for lectures on FHE.
© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in WAHC 2021, 9th Workshop on Encrypted Computing & Applied Homomorphic Cryptography, Associated with the ACM CCS 2021 conference, 15 November 2021, Seoul, South Korea https://doi.org/10.1145/3474366.3486923