Differential Privacy and Natural Language Processing to Generate Contextually Similar Decoy Messages in Honey Encryption Scheme

• URL: http://arxiv.org/abs/2010.15985v1
• Date: Thu, 29 Oct 2020 23:02:32 GMT
• Title: Differential Privacy and Natural Language Processing to Generate Contextually Similar Decoy Messages in Honey Encryption Scheme
• Authors: Kunjal Panchal
• Abstract summary: 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" 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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