# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 6 Experimental Results and Discussion
### Security and Privacy  
Let us consider that an attacker accesses the sensor or steals it physically. To evaluate his/her probability of success, the same genuine and impostor distributions described in Section 6.2 were considered. Table 2 includes the recognition results in terms of Equal Error Rate (EER), which is the error rate where False Matching Rate (FMR) is equal to False Non-Matching Rate (FNMR). Hence, even stealing the sensor node, the probability of successful impersonation attacks is low. Concerning biometric recognition, there is always a trade-off between usability of the solution and recognition performance. If only one instead of two fingers and fewer samples per finger are required from the user, the EER and the usability increase. Table 3 compares these results of the proposed solution with other proposals reported in the literature to implement biometric recognition in wearable sensor nodes. In most of proposals in the literature, the wearables are employed for biometric data acquisition but the recognition algorithm is performed outside the wearable because the algorithms are computationally complex [49; 50; 51; 52]. The fingerprint-based solutions implemented as software in embedded devices (like [36]) have a high power consumption or response time that is not suitable for many sensors, as was shown in Table 1. In order to reduce power consumption and response time, other authors design dedicated hardware for fingerprint recognition [53; 54; 55]. Our proposal can be implemented inside the low-cost wearable sensor node, achieving good distinctiveness without requiring dedicated hardware. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Proposal** & **Platform** & **Freq. (MHz)** & **Extraction Time (s)** & **Matching Time (s)** & **Power (mW)** \\ \hline QFM & ARM Cortex-M3 & 120 & 2.21 & \(2.62\times 10^{-4}\) & 1.32 \\ QFM & ARM Cortex-M4 & 204 & 1.12 & \(1.46\times 10^{-4}\) & 2.50 \\ Minutiae [37] & ARM922T & 200 & 15.74 & 280.01 & 180 \\ Minutiae [38] & NIOS & 33 & 55.88 & 0.15 & \textgreater{}200 \\ Minutiae [39] & Microblaze & 100 & 2.65 & 140.04 & \textgreater{}269 \\ Minutiae [40] & Strong ARM SA-1110 & 206 & 0.56 & 0.01 & \textless{}400 \\ Minutiae [41] & LEON2 without FPU & 50 & 157 & 51 & 500 \\ Minutiae [36] & LEON2 with FPU & 50 & 9.04 & 0.067 & 500 \\ \hline \end{tabular}
\end{table} 
Table 1: Software implementations of fingerprint recognition solutions in embedded systems. \begin{table}
\begin{tabular}{c c c c} \hline
**DB** & **FVC 2000 DB2a** & **FVC 2002 DB1a** \\ \hline
3 samples in enrollment. 2 samples in verification. 2 fingers & EER (\%) & 0.04 & 0.22 \\ \hline \end{tabular}
\end{table} 
Table 2: Recognition results by rejecting captures evaluated with bad quality. Another question concerning privacy is to evaluate if an attacker could know by inspection of the non-volatile memories of the sensor nodes if the same individual is legitimated to use them or not. For that purpose, the biometric Salted Data generated from different samples of fingerprints from an individual who is registered at different nodes are compared with the Salted Data generated from different individuals registered at different nodes. The distributions obtained for the multi-biometric approach based on two fingers and three samples in enrollment and two fingers and two samples in matching are shown in Figure 11. Both distributions coincide because they overlap extensively. Therefore, if an attacker accesses two biometric Salted Data in two different sensor nodes, he/she is not able to discover if they have been generated by the same or different individuals, which is known as unlinkability. For this evaluation, as in the Section 6.2, a virtual database with real fingerprints from fingerprint databases (FVC 2000 DB2a and FVC 2002 DB1a) were associated with real 1024-bit ID responses extracted from the SRAM PUFs in the CC2541 chips.