Abstract
Blockchain is a linearly linked, distributed, and very robust data structure. Originally proposed as part of the Bitcoin distributed stack, it can be applied in a number of fields, most notably in smart contracts, social media, secure IoT, and cryptocurrency mining. It ensures data integrity by distributing strongly encrypted data in widely redundant segments. Each new insertion requires verification and approval by the majority of the users of the blockchain. Both encryption and verification are computationally intensive tasks which cannot be solved with ordinary off-the-shelf CPUs. This has resulted in a renewed scientific interest in secure distributed communication and coordination protocols. Mobile health applications are growing progressively popular and have the enormous advantage of timely diagnosis of certain conditions. However, privacy concerns have been raised as mobile health applications by default have access to highly sensitive personal data. This chapter presents concisely how blockchain can be applied to mobile health applications in order to enhance privacy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi M (2016) TensorFlow: learning functions at scale. ACM SIGPLAN Not 51(9):1–1
Abadi M et al (2016) TensorFlow: a system for large-scale machine learning. In: OSDI, vol 16, pp 265–283
Abbas N, Zhang Y, Taherkordi A, Skeie T (2018) Mobile edge computing: a survey. IEEE Internet Things J 5(1):450–465
Antonopoulos AM (2014) Mastering Bitcoin: unlocking digital cryptocurrencies. O’Reilly Media, Inc, Sebastopol, CA, US
Antonopoulos AM (2017) Mastering Bitcoin: programming the open blockchain. O’Reilly Media, Inc, Sebastopol, CA, US
Barber S, Boyen X, Shi E, Uzun E (2012) Bitter to better – how to make Bitcoin a better currency. In: International conference on financial cryptography and data security. Springer, pp 399–414
Böhme R, Christin N, Edelman B, Moore T (2015) Bitcoin: economics, technology, and governance. J Econ Perspect 29(2):213–238
Cachin C (2016) Architecture of the hyperledger blockchain fabric. In: Workshop on distributed cryptocurrencies and consensus ledgers, vol 310
Drakopoulos G, Gourgaris P, Kanavos A, Makris C (2016) A fuzzy graph framework for initializing k-means. IJAIT 25(6):1–21
Drakopoulos G, Kanavos A, Mylonas P, Sioutas S (2017) Defining and evaluating Twitter influence metrics: a higher order approach in Neo4j. SNAM 71(1):52
Drakopoulos G, Gourgaris P, Kanavos A (2018a) Graph communities in Neo4j: four algorithms at work. Evol Syst 1(1):1–11. https://doi.org/10.1007/s12530-018-9244-x
Drakopoulos G, Liapakis X, Tzimas G, Mylonas P (2018b) A graph resilience metric based on paths: higher order analytics with GPU. In: ICTAI. IEEE
Drakopoulos G, Stathopoulou F, Kanavos A, Paraskevas M, Tzimas G, Mylonas P, Iliadis L (2019) A genetic algorithm for spatiosocial tensor clustering: exploiting TensorFlow potential. Evol Syst 1(1):1–7. https://doi.org/10.1007/s12530-019-09267-8
Kanavos A, Drakopoulos G, Tsakalidis A (2017) Graph community discovery algorithms in Neo4j with a regularization-based evaluation metric. In: WEBIST
Kosba A, Miller A, Shi E, Wen Z, Papamanthou C (2016) Hawk: the blockchain model of cryptography and privacy-preserving smart contracts. In: IEEE symposium on security and privacy. IEEE, pp 839–858
Matthews DG et al (2017) GPflow: a Gaussian process library using TensorFlow. J Mach Learn Res 18(1):1299–1304
Miller D (2018) Blockchain and the internet of things in the industrial sector. IT Prof 20(3):15–18
Nakamoto S (2008) Bitcoin: A peer-to-peer electronic cash system
Pass R, Seeman L, Shelat A (2017) Analysis of the blockchain protocol in asynchronous networks. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 643–673
Puthal D, Malik N, Mohanty SP, Kougianos E, Yang C (2018) The blockchain as a decentralized security framework. IEEE Consum Electron Mag 7(2):18–21
Swan M (2015) Blockchain: blueprint for a new economy. O’Reilly Media, Inc, Sebastopol, CA, US
Underwood S (2016) Blockchain beyond Bitcoin. Commun ACM 59(11):15–17
Wongsuphasawat K et al (2018) Visualizing dataflow graphs of deep learning models in TensorFlow. Trans Vis Comput Graph 24(1):1–12
Zyskind G, Nathan O et al (2015) Decentralizing privacy: using blockchain to protect personal data. In: SPW. IEEE, pp 180–184
Acknowledgements
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Drakopoulos, G., Marountas, M., Liapakis, X., Tzimas, G., Mylonas, P., Sioutas, S. (2020). Blockchain for Mobile Health Applications Acceleration with GPU Computing. In: Vlamos, P. (eds) GeNeDis 2018. Advances in Experimental Medicine and Biology, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-32622-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-32622-7_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32621-0
Online ISBN: 978-3-030-32622-7
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)