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Healthcare Data Breaches: Implications for Digital Forensic Readiness

  • Systems-Level Quality Improvement
  • Published:
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Abstract

While the healthcare industry is undergoing disruptive digital transformation, data breaches involving health information are not usually the result of integration of new technologies. Based on published industry reports, fundamental security safeguards are still considered to be lacking with many documented data breaches occurring as the result of device and equipment theft, human error, hacking, ransomware attacks and misuse. Health information is considered to be one of the most attractive targets for cybercriminals due to its inherent sensitivity, but digital investigations of incidents involving health information are often constrained by the lack of the necessary infrastructure forensic readiness. Following the analysis of healthcare data breach causes and threats, we describe the associated digital forensic readiness challenges in the context of the most significant incident causes. With specific focus on privilege misuse, we present a conceptual architecture for forensic audit logging to assist with capture of the relevant digital artefacts in support of possible future digital investigations.

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References

  1. Cresswell, K. M., and Sheikh, A., Health information technology in hospitals: current issues and future trends. Future Hospital Journal 2(1):50–56, 2015.

    PubMed  PubMed Central  Google Scholar 

  2. Bhavnani, S. P., Parakh, K., Atreja, A., Druz, R., Graham, G. N., Hayek, S. S., Krumholz, H. M., Maddox, T. M., Majmudar, M. D., Rumsfeld, J. S., and Shah, B. R., 2017 Roadmap for Innovation—ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. Journal of the American College of Cardiology 70(21):2696–2718, 2017. https://doi.org/10.1016/j.jacc.2017.10.018.

    Article  PubMed  Google Scholar 

  3. Trustwave, The value of data: a cheap commodity or a priceless asset, 2017.

  4. Islam, S. R., Kwak, D., Kabir, M. H., Hossain, M., and Kwak, K.-S., The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708, 2015.

    Article  Google Scholar 

  5. Verizon, Protected health information data breach report, 2018.

  6. U.S. Department of Health & Human Services (HHS), The HIPAA privacy rule. https://www.hhs.gov/hipaa/for-professionals/privacy/index.html. Accessed 8 April 2018.

  7. Information Commissioner’s Office (ICO), Data Protection Bill 2017. https://ico.org.uk/for-organisations/data-protection-bill/. Accessed 8 April 2018.

  8. European Union (EU), Home Page of EU GDPR. https://www.eugdpr.org/. Accessed 8 April 2018.

  9. Office of the Australian Information Commissioner (OAIC), Privacy Act. https://www.oaic.gov.au/privacy-law/privacy-act/. Accessed 8 April 2018.

  10. Office of the Australian Information Commissioner (OAIC), Notifiable data breaches scheme. https://www.oaic.gov.au/privacy-law/privacy-act/notifiable-data-breaches-scheme. Accessed 8 April 2018.

  11. Singapore Personal Data Protection Commission, Personal data protection act overview. https://www.pdpc.gov.sg/Legislation-and-Guidelines/Personal-Data-Protection-Act-Overview. Accessed 8 April 2018.

  12. Office of the Privacy Commissioner of Canada, The Personal information protection and electronic documents act (PIPEDA). https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/. Accessed 8 April 2018.

  13. Japan Personal Information Protection Commission. Act on the Protection of Personal Information Act No. 57 of (2003), 2005.

  14. U.S. Department of Health & Human Services (HHS). Breach Portal: notice to the secretary of HHS breach of unsecured protected health information. https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf. Accessed 7 April 2018.

  15. Blum, B. I., Orthner, H. F., Implementing health care information systems. In: Implementing Health Care Information Systems. Springer, pp 3–21, 1989.

  16. Medical Identity Fraud Alliance (MIFA), The growing threat of medical identity fraud: a call to action, 2013.

  17. Czeschik C (2018) Black Market Value of Patient Data. In: Claudia Linnhoff-Popien RS, Michael Zaddach (ed) Digital Marketplaces Unleashed. Springer-Verlag. 10.1007/978-3-662-49275-8_78

  18. Dissent, D., 655,000 patient records for sale on the dark net after hacking victims refuse extortion demands. The Daily Dot. https://www.dailydot.com/layer8/655000-patient-records-dark-net/. Accessed 21 April 2018.

  19. Bitglass, Healthcare breach report 2018: Security Procedures Thwart Attacks, 2018.

  20. Moffit, R. E., Health care data breaches: a changing landscape, 2017.

  21. Office of the Australian Information Commissioner (OAIC), Notifiable Data Breaches - Quarterly Statistics Report: January 2018–March 2018., 2018.

  22. VERIS Community Database (VCDB) Project. The VERIS Community Database (VCDB). http://veriscommunity.net/vcdb.html, 2018.

  23. Verizon. Protected health information data breach report, 2015.

  24. Federal Bureau of Investigation (FBI). Table 16 property stolen and recovered. https://ucr.fbi.gov/crime-in-the-u.s/2016/crime-in-the-u.s.-2016/topic-pages/tables/table-16. Accessed 22 April 2018.

  25. Palmer, G., A road map for digital forensic research. In: First Digital Forensic Research Workshop, Utica, pp 27–30, 2001.

  26. Baryamureeba, V., and Tushabe F., The enhanced digital investigation process model. In, 2004.

  27. Carrier, B., Spafford EH An event-based digital forensic investigation framework. In: Digital forensic research workshop, 2004.

  28. Cohen, F., Toward a Science of Digital Forensic Evidence Examination. In Advances in Digital Forensics VI. Springer Berlin Heidelberg, pp 17–35, 2010.

  29. Kohn, M. D., Eloff, M. M., and Eloff, J. H. P., Integrated digital forensic process model. Comput Secur 38:103–115, 2013. https://doi.org/10.1016/j.cose.2013.05.001.

    Article  Google Scholar 

  30. Tan, J., Forensic readiness. Cambridge: @ Stake, 2001, 1–23.

    Google Scholar 

  31. Sachowski, J., Implementing Digital Forensic Readiness: From Reactive to Proactive Process. 1st edn. Syngress, 2016.

  32. Hunt, R., and Zeadally, S., Network Forensics: An Analysis of Techniques, Tools, and Trends. Computer 45(12):36–43, 2012. https://doi.org/10.1109/MC.2012.252.

    Article  Google Scholar 

  33. Khan, S., Gani, A., Wahab, A. W. A., Bagiwa, M. A., Shiraz, M., Khan, S. U., Buyya, R., and Zomaya, A. Y., Cloud log forensics: Foundations, state of the art, and future directions. ACM Computing Surveys (CSUR) 49(1):7, 2016.

    Article  Google Scholar 

  34. Jiang, J., Chen, J., Choo, K.-K. R., Liu, C., Liu, K., Yu, M., A Visualization Scheme for Network Forensics Based on Attribute Oriented Induction Based Frequent Item Mining and Hyper Graph. In Digital Forensics and Cyber Crime. Cham: Springer International Publishing, pp 130–143, 2018.

  35. MacRae, J., and Franqueira V. N., On Locky Ransomware, Al Capone and Brexit. In: International Conference on Digital Forensics and Cyber Crime, Springer, pp 33–45, 2017.

  36. BitCluster, BitCluster. https://www.bit-cluster.com. Accessed 28 April 2018.

  37. Elliptic, Elliptic. https://www.elliptic.co/what-we-do/bitcoin-forensics. Accessed 28 April 2018.

  38. Vargas, J., Bahnsen, A. C., and Villegas, S., Ingevaldson D Knowing your enemies: Leveraging data analysis to expose phishing patterns against a major US financial institution. In: Electronic Crime Research (eCrime), 2016 APWG Symposium on. IEEE, pp 1–10, 2016.

  39. Hamid, I. R. A., Samsudin, N. A., Mustapha, A., and Arbaiy, N., Dynamic Trackback Strategy for Email-Born Phishing Using Maximum Dependency Algorithm (MDA). In Recent Advances on Soft Computing and Data Mining. Cham: Springer International Publishing, pp 263–273, 2017.

  40. Gupta, B. B., Tewari, A., Jain, A. K., and Agrawal, D. P., Fighting against phishing attacks: state of the art and future challenges. Neural Computing and Applications 28(12):3629–3654, 2017. https://doi.org/10.1007/s00521-016-2275-y.

    Article  Google Scholar 

  41. Jayabalan, M., and Daniel T., Continuous and Transparent Access Control Framework for Electronic Health Records: A Preliminary Study. In: International Conference on Information Technology on Information Technology, Information Systems, and Electrical Engineering (ICITISEE 2017), 2017.

  42. Kose, I., Gokturk, M., and Kilic, K., An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance. Applied Soft Computing 36:283–299, 2015.

    Article  Google Scholar 

  43. King, J., Stallings, J., Riaz, M., and Williams, L., To log, or not to log: using heuristics to identify mandatory log events – a controlled experiment. Empirical Software Engineering 22(5):2684–2717, 2017. https://doi.org/10.1007/s10664-016-9449-1.

    Article  Google Scholar 

  44. Protenus, Getting Schooled on Patient Privacy Analytics. https://blog.protenus.com/getting-schooled-on-patient-privacy-analytics. Accessed 3 May 2018.

  45. Cognetyx, The inconvenient truth about patient data security and privacy in healthcare. https://www.cognetyx.com/the-inconvenient-truth-about-patient-data-security-and-privacy-in-healthcare-cognetyxs-new-ambient-cognitive-cyber-surveillance-solution-is-addressing-this-proble/. Accessed 3 May 2018.

  46. Zawoad, S., Dutta, A. K., and Hasan R., SecLaaS: secure logging-as-a-service for cloud forensics. In: Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security. ACM, pp 219–230, 2013.

  47. Nanda, S., Hansen, R. A., Forensics as a Service: Three-tier Architecture for Cloud based Forensic Analysis. In: Parallel and Distributed Computing (ISPDC), 2016 15th International Symposium on, 2016. IEEE, pp 178–183

  48. Zawoad, S., and Hasan, R., Faiot: Towards building a forensics aware eco system for the internet of things. In: Services Computing (SCC), 2015 IEEE International Conference on. IEEE, pp 279–284, 2015.

  49. Raju, B. K., Moharil, B., Geethakumari G FaaSeC: Enabling Forensics-as-a-Service for Cloud Computing Systems. In: 2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC). pp 220–227, 2016.

  50. Yang, H., and Garibaldi, J. M., Automatic detection of protected health information from clinic narratives. Journal of Biomedical Informatics 58:S30–S38, 2015. https://doi.org/10.1016/j.jbi.2015.06.015.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Frühwirt, P., Kieseberg, P., Schrittwieser, S., Huber, M., and Weippl, E., InnoDB database forensics: Enhanced reconstruction of data manipulation queries from redo logs. Information Security Technical Report 17(4):227–238, 2013.

    Article  Google Scholar 

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Acknowledgements

We thank the anonymous reviewers for their valuable comments which helped us to improve the organization and content of this paper.

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Authors and Affiliations

  1. Edith Cowan University, Perth, Australia

    Maxim Chernyshev

  2. University of Kentucky, Lexington, KY, 40506-0224, USA

    Sherali Zeadally

  3. Commonweath Scientific and Industrial Research Organisation (CSIRO), Data61, Melbourne, Australia

    Zubair Baig

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  1. Maxim Chernyshev
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  2. Sherali Zeadally
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  3. Zubair Baig
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Correspondence to Maxim Chernyshev.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Chernyshev, M., Zeadally, S. & Baig, Z. Healthcare Data Breaches: Implications for Digital Forensic Readiness. J Med Syst 43, 7 (2019). https://doi.org/10.1007/s10916-018-1123-2

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