Lightweight and secure encryption using channel measurements

Abstract

Wireless sensor networks are the building blocks of the Internet-of-Things architectures, conveying critical and often sensitive and private information. As these networks often consist of severe resource constrained devices, lightweight encryption mechanisms are of paramount importance for achieving energy efficiency. However, the majority of the proposed algorithms do not fully fulfill the requirements for energy efficiency. Furthermore, key distribution schemes are necessary for their proper operation, making the network vulnerable to adversaries that manage to capture the keys during key exchange. In this work, we propose a scheme where key extraction is performed using channel measurements, thus there is no need for any key distribution mechanism. The derived keys are used for encryption/decryption using the primitives of the compressed sensing theory, which allows encryption and compression simultaneously. The evaluation results show that legitimate nodes experience a very low reconstruction (decryption) error. At the same time, adversaries located at a distance greater than half of the carrier frequency's wavelength, experience a higher error, thus being unable to capture sensitive information.

Secure Wireless Communications Based on Compressive Sensing: A Survey

“…This matrix was evaluated in the Rician fading model [131], which can ensure the physical layer security. Similar to [77], a lightweight encryption scheme was reported [78], in which the information of channel measurements controls the generation of the measurement matrix and thus no key distribution agreement exists.…”

“…CS-Based Secrecy [68] Build up a secure wireless wiretap channel by leveraging the channel asymmetry Secrecy Capacity [69] Quantitatively investigate the lower and upper bounds of the secrecy capacity The Secrecy Based on Distributed CS [126] Design an amplify-forward scheme to cater the distribute nature of wireless sensor network The Secrecy Based on MIMO Precoding [71] Simultaneously maximize the secrecy and the signal-to-noise ratio The Secrecy Based on Circulant Matrix [72] Guarantee the wireless indistinguishability security with some conditions Multicarrier System [73]- [75] Induce artificial noise/channel randomization/particular measurement matrix for the security Cooperative Networks [76] Own the superiority of energy harvesting and high secrecy capacity Establishing Secure Measurement Matrix [77], [78] Design measurement matrix with reciprocal quantization/channel measurements Integrity-Protected CS [79] AES for the encryption of measurements and hash algorithm for integrity checking Capturing Medical Data [80] Capture data firstly and then encrypt them Data Gathering [81]- [84] Combine pseudorandom permutations and symmetric/additively homomorphic encryption Compressed Detection [85], [86] Perform collaborative compressed detection at distributed nodes Adaptive CS for Smart Objects [87] Utilize the information of smart objects to adapt the CS measurement condition Frequency Selection for Static Environment [88], [89] Enlarge the entropy of measured channel and accelerate the rate of generating keys Chaotic CS for Internet of Multimedia Things [90] Realize low-cost sampling and confidentiality preservation Secure Interaction with Cloud [91] Random compressed encryption for the raw data Crowdsensing [92]- [94] Maximize the geographic map coverage and protect the participants' trace privacy Smart Grid [95]- [97] Construct a secret measurement matrix for joint encryption, sampling, and compression Wireless Body Area Networks [98]- [100] Exploit chaotic CS for energy saving and data security a long delay off over fading channels…”

Physical layer security using an adaptive modulation scheme for improved confidentiality

“…Data transmission commences with the exchange of pilot signals between them in same time slot or consecutive two time slots in a full or half-duplex system. Depending on the received pilot signals, impulse response of the channel is estimated without acknowledgement [17]. Owing to the reciprocity nature of the time division duplex channels being considered in this case, the instantaneous magnitude and phase of the channel between Tx and Rx is known only to them and concealed from eavesdroppers.…”