Ezocrypt is an encryption framework for 1:1 datasets with multiple sensitive attributes using slicing and heap bucketization techniques to achieve higher privacy and less utility loss with high performance also protects privacy against the following attacks such as background knowledge attacks, quasi-identifier attacks, membership disclosure attacks, non-membership disclosure attack, and fingerprint correlation attack.

Generate random images with Chaotic map mathematical functions to encrypt all type of medical images with any specification when data is in transit and at rest.