Robust fingerprinting of genomic databases.
Robust fingerprinting of genomic databases.
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DOI：
10.1093/bioinformatics/btac243
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发表时间：
2022-06-24
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通过提供识别数据泄漏源的方法，数据库指纹识别已被广泛用于阻止未经授权的数据重新分发。然而，在共享基因组数据库时，没有旨在实现责任保证的指纹识别方案。因此，我们有动力通过设计一种专门用于基因组数据库的香草指纹方案来填补这一空白。此外，由于恶意的基因组数据库接收者可能会通过发起有效的关联攻击来破坏嵌入的指纹(扭曲隐藏标记，即嵌入的指纹比特串)，从而利用基因组数据之间的内在相关性(例如孟德尔定律和连锁不平衡)，我们还通过开发缓解技术来增强普通方案，以实现针对关联攻击的稳健的基因组数据库指纹识别。通过使用真实的基因组数据库进行实验，我们首先证明了针对基因组数据库指纹方案的相关攻击是非常强大的。具体地说，相关攻击可以通过造成小的效用损失(例如，通过P值测量的SNP-表现型关联的数据库准确性和一致性)来扭曲超过一半的指纹位。接下来，我们通过实验证明，我们提出的缓解技术可以有效地缓解相关攻击。我们验证了攻击者几乎不能损害大部分指纹比特，即使它在数据库实用程序降级方面付出了更高的代价。例如，在大约24%的准确率损失和20%的SNP-表型关联一致性损失的情况下，攻击者只能扭曲大约30%的指纹比特，这不足以避免被指控。我们还表明，所提出的缓解技术还保留了共享基因组数据库的实用性，例如，缓解技术仅导致大约3%的准确率损失。Https://github.com/xiutianxi/robust-genomic-fp-github.
Database fingerprinting has been widely used to discourage unauthorized redistribution of data by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint (distort the steganographic marks, i.e. the embedded fingerprint bit-string) by launching effective correlation attacks, which leverage the intrinsic correlations among genomic data (e.g. Mendel’s law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks. Via experiments using a real-world genomic database, we first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g. database accuracy and consistency of SNP–phenotype associations measured via P-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP–phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases, e.g. the mitigation techniques only lead to around 3% loss in accuracy. https://github.com/xiutianxi/robust-genomic-fp-github.
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