Due to its hereditary nature, genomic data is not only linked to its owner but to that of close relatives as well. As a result, its sensitivity does not really degrade over time; in fact, the relevance of a genomic sequence is likely to be longer than the security provided by encryption. This prompts the need for specialized techniques providing long-term security for genomic data, yet the only available tool for this purpose is GenoGuard (, 2015). By relying on Honey Encryption, GenoGuard is secure against an adversary that can brute force all possible keys; i.e., whenever an attacker tries to decrypt using an incorrect password, she will obtain an incorrect but plausible looking decoy sequence. In this paper, we set to analyze the real-world security guarantees provided by GenoGuard; specifically, assess how much more information does access to a ciphertext encrypted using GenoGuard yield, compared to one that was not. Overall, we find that, if the adversary has access to side information in the form of partial information from the target sequence, the use of GenoGuard does appreciably increase her power in determining the rest of the sequence. We show that, in the case of a sequence encrypted using an easily guessable (low-entropy) password, the adversary is able to rule out most decoy sequences, and obtain the target sequence with just 2.5 of it available as side information. In the case of a harder-to-guess (high-entropy) password, we show that the adversary still obtains, on average, better accuracy in guessing the rest of the target sequences than using state-of-the-art genomic sequence inference methods, obtaining up to 15 improvement in accuracy.
Long-term security. As the sensitivity of genomic data does not degrade over time, access to an individual's genome poses a threat to her descendants, even years after she has deceased. To the best of our knowledge, GenoGuard @cite_27 is the only attempt to provide long-term security. GenoGuard, reviewed in , relies on Honey Encryption @cite_35 , aiming to provide confidentiality in the presence of brute-force attacks; it only serves as a storage mechanism, i.e., it does not support selective retrieval or testing on encrypted data (as such, it is not composable'' with other techniques supporting privacy-preserving testing or data sharing). In this paper, we provide a security analysis of GenoGuard. In parallel to our work, @cite_15 recently propose attacks against probability model transforming encoders, and also evaluate them on GenoGuard. Using machine learning, they train a classifier to distinguish between the real and the decoy sequences, and exclude all decoy data for approximately 48
@cite_35 @cite_27 @cite_15
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We introduce honey encryption (HE), a simple, general approach to encrypting messages using low min-entropy keys such as passwords. HE is designed to produce a ciphertext which, when decrypted with any of a number of incorrect keys, yields plausible-looking but bogus plaintexts called honey messages. A key benefit of HE is that it provides security in cases where too little entropy is available to withstand brute-force attacks that try every key; in this sense, HE provides security beyond conventional brute-force bounds. HE can also provide a hedge against partial disclosure of high min-entropy keys. Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been aggravating the potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term, but given the multi-decade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool, called Geno Guard, for providing strong protection for genomic data both today and in the long term. Geno Guard incorporates a new theoretical framework for encryption called honey encryption (HE): it can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can be applied to messages from, unfortunately, a very restricted set of probability distributions. Therefore, Geno Guard addresses the open problem of applying HE techniques to the highly non-uniform probability distributions that characterize sequences of genetic data. In Geno Guard, a potential adversary can attempt exhaustively to guess keys or passwords and decrypt via a brute-force attack. We prove that decryption under any key will yield a plausible genome sequence, and that Geno Guard offers an information-theoretic security guarantee against message-recovery attacks. We also explore attacks that use side information. Finally, we present an efficient and parallelized software implementation of Geno Guard.