  Honey Encryption is an approach to encrypt the messages using low min-entropy
keys, such as weak passwords, OTPs, PINs, credit card numbers. The ciphertext
is produces, when decrypted with any number of incorrect keys, produces
plausible-looking but bogus plaintext called "honey messages". But the current
techniques used in producing the decoy plaintexts do not model human language
entirely. A gibberish, random assortment of words is not enough to fool an
attacker; that will not be acceptable and convincing, whether or not the
attacker knows some information of the genuine source.
  In this paper, I focus on the plaintexts which are some non-numeric
informative messages. In order to fool the attacker into believing that the
decoy message can actually be from a certain source, we need to capture the
empirical and contextual properties of the language. That is, there should be
no linguistic difference between real and fake message, without revealing the
structure of the real message. I employ natural language processing and
generalized differential privacy to solve this problem. Mainly I focus on
machine learning methods like keyword extraction, context classification,
bags-of-words, word embeddings, transformers for text processing to model
privacy for text documents. Then I prove the security of this approach with
e-differential privacy.
