# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 1 Introduction  
Security is becoming greatly important in sensor networks since they are employed in critical tasks such as military, industrial, home, and health applications, among others. Security goals usually addressed are confidentiality, integrity, freshness, authentication, and non-repudiation. The data sensed and transmitted should be confidential if they are related to sensitive information (as is the case with geolocation data in the deployment of military forces) and should maintain integrity to prove they have not been altered or introduced by an adversary. Data confidentiality and integrity can be achieved by symmetric key cryptography, which uses the same key for encryption and decryption. Symmetric key cryptography is preferred to asymmetric or public key cryptography at sensor nodes because it requires fewer computation resources and lower power consumption. As drawback, key management is more complex in symmetric key cryptography. A widely adopted solution, suitable for sensor nodes,is a key pre-distribution scheme that stores a master secret key in all sensor nodes before they are deployed. After deployed, any pair of sensor nodes can use the global master secret key to negotiate a new shared key [1]. The problem of using a master key is that the whole network security can be compromised if a sensor node is compromised. Hence, highly secure non-volatile memories can be employed to store the secret key. However, they are more expensive than standard memories and several techniques have been reported to attack and extract data from them [2]. Trusted hardware can be used for secure storage [3; 4; 5]. However, it is impractical for low-cost sensor nodes. Furthermore, malware has been demonstrated to be run in these hardware enclaves [6]. Instead of storing a secret key, the solution proposed in this paper is to reconstruct it by using a bit string obtained from the physical identity of the sensor node. A recent research area is to obtain bit strings from Physically Unclonable Functions (PUFs), which allow generating unique, distinctive and unpredictable identifiers. They are produced by the manufacturing process variability of the sensor hardware [7]. A sensor with PUFs cannot be cloned physically because the manufacturing process introduces variations that are specific to each sensor. Hence, a compromised-key attack is more difficult (because the key is not stored anyway). In addition, counterfeiting (false copies of sensors) attacks are more difficult because the physical identity of the sensor node can be authenticated [8]. Among the electronic circuits employed as PUFs (SRAMs, latches, D Flip-Flops, arbiters, ring oscillators, etc. ), SRAM PUFs are used in this work since no additional circuitry is needed. Commonly, SRAMs are readily present in sensors. Hence, using SRAM PUFs is low-cost from the hardware point of view. The use of SRAMs as PUFs involves reading the start-up values when the memory is switched on and no data is written. The start-up values of one SRAM are difficult to be generated by another SRAM or ascertained by someone willing to clone the SRAM [9]. A well-known approach to reconstruct keys from noisy data (such as those provided by PUFs) is the use of Helper Data algorithms [10; 11]. They are based on the storage of ideally non-sensitive information named as Helper Data. Sensitive information such as the secret key is obfuscated with the PUF response to generate Helper Data that do not reveal information about the secret key. Helper Data are generated and stored at an enrollment stage. Then, whenever the key is required, a key reconstruction stage is carried out. The most popular scheme is the Code Offset-based Helper Data algorithm [11], where an Error Correcting Code (ECC) is employed to cope with noise. The secret key is encoded according to the ECC. At key reconstruction, a new PUF response is obtained that will not be exactly the same as the response used at the enrolment stage but very similar.