AI Summary & Key Takeaways
AI InsightOur work shows that more realistic attack scenarios must be considered in the design of genetic security systems.
📌 3 Key Takeaways
Abstract
With the decreasing cost and availability of human genome sequencing, genomic privacy becomes an important issue. Several methods have been proposed in the literature to overcome these problems including cryptographic and privacy preserving data mining methods: homomorphic encryption, cryptographic hardware. In a recent work, Barman et. al studied privacy threats and practical solutions considering an SNP based scenario. The authors introduced a new protocol where a malicious medical center processes an active attack in order to retrieve genomic data of a given patient. The authors have mentioned that this protocol provides a trade-off between privacy and practicality. In this paper, we first give an overview of the system for SNP based risk calculation. We provide the definitions of privacy threats and briefly Barman et al.’s protocol and their solution. The authors proposed to use a weighted sum method of SNP coefficients for calculating disease tendency. They argue that the specific choice of the bases would prevent unique identification of SNPs. Our main observation is that this is not true. Contrary to the security claim, SNP combinations can be identified uniquely in many different scenarios. Our method exploits a pre-computed look-up table for retrieving SNPs’ values from the test result. An attacker can obtain all SNP values of a given patient by using the pre-computed look-up table. We provide practical examples of weights and pre-computed tables. We also mention that even in the case where the table is large and the attacker can not handle at one time, he can still gather information using multi queries. Our work shows that more realistic attack scenarios must be considered in the design of genetic security systems.