Securing First-Hop Data Provenance for Bodyworn Devices Using Wireless Link Fingerprints

Abstract

Abstract-Wireless bodyworn sensing devices are fast becoming popular for fitness, sports training and personalized healthcare applications. Securing data generated by these devices is essential if they are to be integrated into the current health infrastructure and employed in medical applications. In this paper, we propose a mechanism to secure data provenance for these devices by exploiting spatio-temporal characteristics of the wireless channel that these devices use for communication. Our solution enables two parties to generate closely matching 'link fingerprints' which uniquely associate a data session with a wireless link such that a third party can later verify the details of the transaction, particularly the wireless link on which the data was transmitted. These fingerprints are very hard for an eavesdropper to forge, they are lightweight compared to traditional provenance mechanisms, and enable interesting security properties such as accountability, non-repudiation, and resist man-in-the-middle attacks. We validate our technique with experiments using bodyworn sensors in scenarios approximating actual device deployment, and present some extensions which reduce energy consumption. We believe this is a promising first step towards using wireless-link characteristics for data provenance in body area networks.

Implementation of Link Fingerprint Verification and Data Provenance using RSSI

“…The projected solution has mitigated time complexity contrasted with other best resolutions. The power computations are exhibited too demonstrating alluring outcomes when contrasted with the previously existed work in [7].…”

“…Besides, the power benchmarks of MICAz bits utilized in the literature are connected to the displayed protocols. The energy utilization for AES-128 encryption (128 bits), SHA-1 Hash (64 bits), ECDSA-160 Sign and Transmit 1 part are 1.83 J, 154 J, 52 J and 0.6 J individually [7]. As the decrypting is completed at the server, the power figurings are not accomplished for the server.…”

“…2) IoT nodes provide few bytes as headers to the payload which comprises of encrypted link -fingerprints. Table 4 and 5 discloses the power exploitation at each node level when the packet is transmitted from IoT node 1 to IoT node 3 through IoT node 1 according to the hash data and session identifiers as part of protocols used conventionally in [7]. Table 4: Energy consumption at IoT node level for link fingerprints transmission to the server Table 5: Energy consumption at IoT node level for data provenance protocol…”

“…Both the physical and social layer data are consolidated for acknowledging fast content spread in gadget to-gadget vehicle-to-vehicle (D2D-V2V)-based IoV systems. In [7] paper, verifying the data provenance is accomplished by utilizing the RSSI values gotten by a static base station and a versatile body-worn gadget. Performed investigations demonstrate that exceptionally associated fingerprints are obtained.…”

Physical layer authentication of off‐body channels by probabilistic neural networks

“…Thanks to a relatively high stationarity of on-body channels, a reasonable probability of radio-frequency fingerprinting identification can be reached. 13 The authentication scheme based on radio-frequency fingerprints can be understood as a special case of the scene analysis. During the calibration phase, fingerprints of electromagnetic scene are collected.…”