Deception-Based Data Protection Using Multifile Encryption and Honey Diffusion

The importance of encryption algorithms has greatly increased as an increasing amount of communication and storage are becoming digital. Typical encryption algorithms operate by encrypting a file or message with a key which is distributed to trusted parties. However, if a malicious or untrustworthy individual is able to discover the key, any data protected by these algorithms becomes compromised. To address this, deception-based algorithms have been suggested, where false or meaningless data is mixed together with real data so that malicious parties will be unable to determine whether any data they have managed to obtain is legitimate. In this paper, we present two novel deception-based algorithms, each designed to address the other's limitations. The first algorithm uses a multifile encryption algorithm that merges multiple files, each with their own key, into a single generator file that outputs one of the original files when given their corresponding password. The second algorithm, Honey Diffusion, uses a diffusion model that generates a near-exact replica of the real data when given the correct password, and similar but distinct data when given an incorrect password. We also demonstrate some use cases of these algorithms.