基于几何哈希的内存高效模糊指纹库

Sungju Lee, Daesung Moon, Hanna Choi, Yongwha Chung
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引用次数: 14

摘要

利用几何散列技术的思想,解决了模糊指纹库中的自动对齐问题。虽然这种解决方案可以提供更高的验证精度,但由于哈希表的大小较大,它需要更多的内存空间。在本文中,我们提出了一种在不牺牲验证准确性的情况下,通过使用时间-内存权衡来减少哈希表大小的方法。也就是说,我们的方法不是在登记阶段生成完整的哈希表,而是在验证阶段“即时”生成登记哈希表。通过仔细选择基集,可以进一步减小哈希表的大小。实验结果表明,该方法可以在不牺牲验证精度和安全性的前提下降低静态和动态内存需求。
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Memory-Efficient Fuzzy Fingerprint Vault based on the Geometric Hashing
One of the solutions to the auto-alignment problem in the fuzzy fingerprint vault exploited the idea of the geometric hashing technique. Although this solution can provide higher verification accuracy, it requires more memory space due to the large size of the hash table. In this paper, we propose an approach to reduce the size of the hash table by using the time-memory tradeoff without sacrificing the verification accuracy. That is, instead of generating the full hash table at the enrollment phase, our approach generates the enrollment hash table "on-the-fly" at the verification phase. The size of the hash table can be reduced further by selecting the basis set carefully. Based on the experimental results, we confirm that the proposed approach can reduce both the static and the dynamic memory requirements without sacrificing both the verification accuracy and the security level.
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