Spanish National Research Council · University of Seville
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IMSE-CNM in Digital.CSIC


In all publications
Author: Arcenegui Almenara, Javier
Year: Since 2002
All publications
Trusted Cameras on Mobile Devices Based on SRAM Physically Unclonable Functions
R. Arjona, M.A. Prada-Delgado, J. Arcenegui and I. Baturone
Journal Paper - Sensors, vol. 18, no. 10, art, 3352, 2018
MDPI    DOI: 10.3390/s18103352    ISSN: 1424-8220    » doi
Nowadays, there is an increasing number of cameras placed on mobile devices connected to the Internet. Since these cameras acquire and process sensitive and vulnerable data in applications such as surveillance or monitoring, security is essential to avoid cyberattacks. However, cameras on mobile devices have constraints in size, computation and power consumption, so that lightweight security techniques should be considered. Camera identification techniques guarantee the origin of the data. Among the camera identification techniques, Physically Unclonable Functions (PUFs) allow generating unique, distinctive and unpredictable identifiers from the hardware of a device. PUFs are also very suitable to obfuscate secret keys (by binding them to the hardware of the device) and generate random sequences (employed as nonces). In this work, we propose a trusted camera based on PUFs and standard cryptographic algorithms. In addition, a protocol is proposed to protect the communication with the trusted camera, which satisfies authentication, confidentiality, integrity and freshness in the data communication. This is particularly interesting to carry out camera control actions and firmware updates. PUFs from Static Random Access Memories (SRAMs) are selected because cameras typically include SRAMs in its hardware. Therefore, additional hardware is not required and security techniques can be implemented at low cost. Experimental results are shown to prove how the proposed solution can be implemented with the SRAM of commercial Bluetooth Low Energy (BLE) chips included in the communication module of the camera. A proof of concept shows that the proposed solution can be implemented in low-cost cameras.

A PUF-and biometric-based lightweight hardware solution to increase security at sensor nodes
R. Arjona, M.A. Prada-Delgado, J. Arcenegui and I. Baturone
Journal Paper - Sensors, vol. 18, no. 8, article 2429, 2018
MDPI AG    DOI: 10.3390/s18082429    ISSN: 1424-8220    » doi
Security is essential in sensor nodes which acquire and transmit sensitive data. However, the constraints of processing, memory and power consumption are very high in these nodes. Cryptographic algorithms based on symmetric key are very suitable for them. The drawback is that secure storage of secret keys is required. In this work, a low-cost solution is presented to obfuscate secret keys with Physically Unclonable Functions (PUFs), which exploit the hardware identity of the node. In addition, a lightweight fingerprint recognition solution is proposed, which can be implemented in low-cost sensor nodes. Since biometric data of individuals are sensitive, they are also obfuscated with PUFs. Both solutions allow authenticating the origin of the sensed data with a proposed dual-factor authentication protocol. One factor is the unique physical identity of the trusted sensor node that measures them. The other factor is the physical presence of the legitimate individual in charge of authorizing their transmission. Experimental results are included to prove how the proposed PUF-based solution can be implemented with the SRAMs of commercial Bluetooth Low Energy (BLE) chips which belong to the communication module of the sensor node. Implementation results show how the proposed fingerprint recognition based on the novel texture-based feature named QFingerMap16 (QFM) can be implemented fully inside a low-cost sensor node. Robustness, security and privacy issues at the proposed sensor nodes are discussed and analyzed with experimental results from PUFs and fingerprints taken from public and standard databases.

Using Physical Unclonable Functions for Internet-of-Thing Security Cameras
R. Arjona, M.A. Prada-Delgado, J. Arcenegui and I. Baturone
Journal Paper - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), vol. 242, pp 144-153, 2018
SPRINGER    DOI: 10.1007/978-3-319-93797-7_16    » doi
This paper proposes a low-cost solution to develop IoT security cameras. Integrity and confidentiality of the image data is achieved by using the cryptographic modules that implement symmetric key-based techniques which are usually available in the hardware of the IoT cameras. The novelty of this proposal is that the secret key required is not stored but reconstructed from public data and from the start-up values of a SRAM in the camera hardware acting as a PUF (Physical Unclonable Function), so that the physical authenticity of the camera is also ensured. The variability of the start-up values of the SRAM is also exploited to change the IV (initialization vector) in the encryption algorithm, thus increasing security. All the steps to be carried out by the IoT camera at enrollment and normal operation can be included in a simple firmware to be executed by the camera. In addition, this firmware can be trustworthy updated. There is no need to include specific hardware (such as TPMs) but only an SRAM is needed which could be powered down and up by firmware.

Demonstrator of a Fingerprint Recognition Algorithm into a Low-Power Microcontroller
J. Arcenegui, R. Arjona and I. Baturone
Conference - Conference on Design and Architectures for Signal and Image Processing DASIP 2017
A demonstrator has been developed to illustrate the performance of a lightweight fingerprint recognition algorithm based on the fingerprint feature QFingerMap16, which is extracted from a window of the directional image (containing 16 direction values) centered at the convex core of the fingerprint. The algorithm has been implemented into a low-power ARM Cortex-M3 microcontroller included in a Texas Instruments LaunchPad CC2650 evaluation kit. It has been also implemented in a Raspberry Pi 2 so as to show the results obtained at the successive steps of the recognition process with the aid of a Graphical User Interface (GUI).

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