We present datasets produced by an instance of a new approach in data security, known as HbHAI (Hash-based Homomorphic Artificial Intelligence). This disruptive approach enables processing data under their encrypted form without the limitations and drawbacks that exist for conventional homomorphic data analysis techniques to date (CKKS and BFV schemes). HbHAI is based on a new class of key-dependent hash functions proposed and formalized in [3] that naturally preserve the similarity properties, most AI algorithms rely on. Our instance of HbHAI techniques is not yet public as it is in the process of being protected industrially. However, to enable an initial public assessment, this paper presents several datasets which will be published in a very near future. Among its many features, our HbHAI instance reduces the size of data at a compression ratio of at least 3. While strongly preserving data security and privacy as formalized in [3], our instance reduces storage space and computing time for native, “off-the-shelf” AI algorithms effectively.