Toward Privacy-Aware Healthcare Data Fusion Systems

  • Isam Mashhour Al JawarnehEmail author
  • Paolo Bellavista
  • Luca Foschini
  • Rebecca Montanari
  • Javier Berrocal
  • Juan M. Murillo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1016)


Mobile wearable and sensor-enabled devices offer an opportunity for deluging unprecedented amount of health-related data that is beneficial in health and caregiving research. Fusing data ingested throughout various heterogeneous channels is essential for better provisioning novel healthcare solutions. However, this is typically challenged by privacy-awareness. For example, the European Commission throughout its call-for-proposals always stresses a requirement that provisioned solutions should consider privacy and should boost security- and privacy-awareness in cloud computing environments. Current solutions either do not consider privacy requirements or provide solutions that are mostly ad hoc and patch efforts. In this position paper, we motivate the adoption of Blockchain technologies for providing privacy-awareness to novel healthcare data fusion solutions. Our envisioned solution is proposed on top of current state-of-the-art blockchain and big data representatives, specifically Hyperledger Fabric and Apache Spark.


Blockchain Privacy-aware Healthcare Spark Hyperledger Fabric Context-aware 


  1. 1.
    E. Commission. (2017, 1/5/2018). TOPIC: Smart and healthy living at home.
  2. 2.
    Lin, Q., Zhang, D., Connelly, K., Ni, H., Yu, Z., Zhou, X.: Disorientation detection by mining GPS trajectories for cognitively-impaired elders. Pervasive Mob. Comput. 19, 71–85 (2015)CrossRefGoogle Scholar
  3. 3.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. Presented at the Proceedings of the 2nd USENIX conference on Hot topics in cloud computing, Boston, MA (2010)Google Scholar
  4. 4.
    Wang, M., Perera, C., Jayaraman, P.P., Zhang, M., Strazdins, P., Shyamsundar, R., et al.: City data fusion: sensor data fusion in the internet of things. Int. J. Distrib. Syst. Technol. (IJDST) 7, 15–36 (2016)CrossRefGoogle Scholar
  5. 5.
    Sezer, O.B., Dogdu, E., Ozbayoglu, A.M.: Context-aware computing, learning, and big data in internet of things: a survey. IEEE Internet Things J. 5, 1–27 (2018)CrossRefGoogle Scholar
  6. 6.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, New York (2010). Scholar
  7. 7.
    Berrocal, J., Garcia-Alonso, J., Murillo, J.M., Canal, C.: Rich contextual information for monitoring the elderly in an early stage of cognitive impairment. Pervasive Mob. Comput. 34, 106–125 (2017)CrossRefGoogle Scholar
  8. 8.
    Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008). Accessed Sept 2018
  9. 9.
    Wood, G.: Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj. Yellow Paper 151, 1–32 (2014)Google Scholar
  10. 10.
    Merkle, R.C.: Protocols for public key cryptosystems. In: 1980 IEEE Symposium on Security and Privacy, p. 122 (1980)Google Scholar
  11. 11.
    Codd, E.F.: A relational model of data for large shared data banks. Commun. ACM 13, 377–387 (1970)CrossRefGoogle Scholar
  12. 12.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)CrossRefGoogle Scholar
  13. 13.
    White, T.: Hadoop: The Definitive Guide. O’Reilly Media Inc., Sebastopol (2009)Google Scholar
  14. 14.
    Banker, K.: MongoDB in Action: Manning (2012)Google Scholar
  15. 15.
    Anderson, J.C., Lehnardt, J., Slater, N.: CouchDB: The Definitive Guide: Time to Relax. O’Reilly Media, Newton (2010)Google Scholar
  16. 16.
    Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. Presented at the Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation, San Jose, CA (2012)Google Scholar
  17. 17.
  18. 18.
    Trattner, C., Elsweiler, D.: Food Recommender Systems: Important Contributions, Challenges and Future Research Directions, arXiv preprint arXiv:1711.02760 (2017)
  19. 19.
    Yue, X., Wang, H., Jin, D., Li, M., Jiang, W.: Healthcare data gateways: found healthcare intelligence on blockchain with novel privacy risk control. J. Med. Syst. 40, 218 (2016)CrossRefGoogle Scholar
  20. 20.
    Azaria, A., Ekblaw, A., Vieira, T., Lippman, A.: MedRec: using blockchain for medical data access and permission management. In: 2016 2nd International Conference on Open and Big Data (OBD), pp. 25–30 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Dipartimento di Informatica – Scienza e IngegneriaUniversity of BolognaBolognaItaly
  2. 2.Escuela PolitécnicaUniversidad de ExtremaduraCáceresSpain

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