Skip to main content
SpringerLink
Log in

Fraud Detection in Medical Insurance Claim with Privacy Preserving Data Publishing in TLS-N Using Blockchain

  • Conference paper
  • First Online:
Advances in Computing and Data Sciences (ICACDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1045))

Included in the following conference series:

Abstract

Insurance fraud is one of the most serious problem that insurers, consumers and regulators witnessed in the last couple of years affecting availability of insurances. Increase in the cost of the companies, inflated premium is caused by the number of frauds in the medical insurance sector threatening the viability of the insurance companies and also an adverse effect on the profit they incur. Forged documents submitted for claim settlement gets verified due to limited resource availability. Enabling TLS-N in the conversation between the insurance company, policy holder and medical institution curtail life insurance frauds. Data privacy must be given utmost importance while we handle medical insurance scenarios, TLS-N with privacy preserving data publishing schemes facilitates privacy and mitigates content hiding attack. These records stored in blockchain keeps track of the policy details, the medical reports and insurance claims over the period of validity, helps detecting fraud in the insurance claim.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chudgar, D.: Life Insurance Fraud–Risk Management and Fraud Prevention (2013). papers.ssrn.com

  2. Ritzdorf, H., et al.: TLS-N: Non-repudiation over TLS Enabling Ubiquitous Content Signing for Disintermediation. IACR ePrint report 578 (2017)

    Google Scholar 

  3. Mazonka, O.: Blockchain: simple explanation. J. Ref. (2016)

    Google Scholar 

  4. Nakamoto, S., et al.: Bitcoin: A peer-to-peer electronic cash system (2008). Working Paper

    Google Scholar 

  5. Sajana, P., Sindhu, M., Sethumadhavan, M.: On Blockchain Applications: Hyperledger Fabric And Ethereum. Int. J. Pure Appl. Math. 118(18), 2965–2970 (2018)

    Google Scholar 

  6. Buterin, V.: A next-generation smart contract and decentralized application platform. White paper (2014)

    Google Scholar 

  7. Fekkes, L., et al.: Comparing Bitcoin and Ethereum (2018)

    Google Scholar 

  8. Katz, J., et al.: Handbook of Applied Cryptography. CRC Press, Boca Raton (1996)

    Google Scholar 

  9. Delignat-Lavaud, A., et al.: Implementing and proving the TLS 1.3 record layer. In: 2017 IEEE Symposium on Security and Privacy (SP). IEEE (2017)

    Google Scholar 

  10. Dierks, T., Allen, C.: RFC 2246: The TLS protocol version 1.0, January 1999. Status: Proposed Standard (1996)

    Google Scholar 

  11. Dierks, T., Rescorla, E.: “IETF RFC 4346.” The Transport Layer Security (TLS) Protocol Version 1 (2006)

    Google Scholar 

  12. Rescorla, E.: The transport layer security (TLS) protocol version 1.3. No. RFC 8446 2018

    Google Scholar 

  13. Purohit, M., Kaushik V., Vasudevan, S.K.: Enhanced Link Redirection Interface for Secured Browsing Using Web Browser Extensions. Editorial Preface 4 no. 10 (2013)

    Google Scholar 

  14. Sreevidya, B., Rajesh, M., Sasikala, T.: Performance analysis of various anonymization techniques for privacy preservation of sensitive data. In: Hemanth, J., Fernando, X., Lafata, P., Baig, Z. (eds.) ICICI 2018. LNDECT, vol. 26, pp. 687–693. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-03146-6_77

    Chapter  Google Scholar 

  15. Steinfeld, R., Bull, L., Zheng, Y.: Content extraction signatures. In: Kim, K. (ed.) ICISC 2001. LNCS, vol. 2288, pp. 285–304. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45861-1_22

    Chapter  Google Scholar 

  16. Bull, L., Squire, D., Zheng, Y.: A hierarchical extraction policy for content extraction signatures. Int. J. Digit. Libr. 4(3), 208–222 (2004)

    Article  Google Scholar 

  17. Wang, C., et al.: A new forward secure content extraction signature scheme. In: 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE (2015)

    Google Scholar 

  18. Chang, E.-C., Lim, C.L., Xu, J.: Short redactable signatures using random trees. In: Fischlin, M. (ed.) CT-RSA 2009. LNCS, vol. 5473, pp. 133–147. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00862-7_9

    Chapter  Google Scholar 

  19. FeiFei, Z., LiFeng, D., Kun, W., Yang, L.: Study on privacy protection algorithm based on k-anonymity. Phys. Procedia Elsevier 33, 483–490 (2012)

    Article  Google Scholar 

  20. Wang, K., Chen, R., Fung, B.C., Yu, P.S.: Privacy-preserving data publishing: a survey on recent developments. ACM Computing Surveys (2010)

    Google Scholar 

  21. Dubovitskaya, A., Xu, Z., Ryu, S., Schumacher, M., Wang, F.: Secure and trustable electronic medical records sharing using blockchain. In: AMIA Annual Symposium Proceedings 2017, p. 650 (2017)

    Google Scholar 

  22. Ajun, C.: Slicing: a new approach to privacy preserving data publishing. IJESC Springer (2015)

    Google Scholar 

  23. Goryczka, S., Xiong, L., Fung, B.C.M.: m-Privacy for Collaborative Data Publishing. IEEE Trans. Knowl. Data Eng. 26(10), 2520–2533 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TIFAC-CORE in Cyber Security, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Thanusree Mohan & K. Praveen

Authors
  1. Thanusree Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Praveen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thanusree Mohan .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Computer Science and Engineering, Jaypee Institute of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. ÚTIA AV ČR, Institute of Information Theory and Automation, Prague 8, Praha, Czech Republic

    Jan Flusser

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. CSE Department, Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

    Rekha Kashyap

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mohan, T., Praveen, K. (2019). Fraud Detection in Medical Insurance Claim with Privacy Preserving Data Publishing in TLS-N Using Blockchain. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-13-9939-8_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9939-8_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9938-1

  • Online ISBN: 978-981-13-9939-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation