Fingerprint provides an extremely convenient way of identification for a wide range of real-life applications owing to its universality, uniqueness, collectability, and invariance. However, digitized fingerprints may reveal the privacy of individuals. Differential privacy is a promising privacy-preserving solution that is enforced by injecting random noise into preserved objects, such that an adversary with arbitrary background knowledge cannot infer private input from the noisy results. This study proposes FinPrivacy, a privacy-preserving mechanism for fingerprint identification. This mechanism utilizes the low-rank matrix approximation to reduce the dimensionality of fingerprint and the exponential mechanism to carefully determine the value of the optimal rank. Thereafter, FinPrivacy injects Laplace noise to the singular values of the approximated singular matrix, thereby trading off between privacy and utility. Analytic proofs and results of the comparative experiments demonstrate that FinPrivacy can simultaneously enforce ɛ-differential privacy and maintain an efficient fingerprint recognition.

中文翻译：

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FinPrivacy：指纹识别的隐私保护机制

指纹由于其普遍性、唯一性、可收集性和不变性，为广泛的现实生活应用提供了一种极其方便的识别方式。然而，数字化指纹可能会泄露个人隐私。差分隐私是一种很有前途的隐私保护解决方案，它通过将随机噪声注入到保存的对象中来强制执行，这样具有任意背景知识的对手就无法从噪声结果中推断出私人输入。本研究提出了 FinPrivacy，一种用于指纹识别的隐私保护机制。该机制利用低秩矩阵逼近来降低指纹的维数和指数机制来仔细确定最优秩的值。此后，FinPrivacy 将拉普拉斯噪声注入到近似奇异矩阵的奇异值中，从而在隐私和实用性之间进行权衡。分析证明和比较实验的结果表明，FinPrivacy 可以同时执行 ɛ-差分隐私并保持有效的指纹识别。