Privacy Preservation of Electronic Health Record: Current Status and Future Direction

Recent developments in health sector have made it possible to collect, store, manage, and share medical data in large scale. Managing and sharing of health record is primarily requirement in electronic health record software, however, reusability of electronic health records in distributive environment or access by third party must maintain principle of database system and implement the guidelines of international privacy policy standards and regulations. Privacy preservation is the major concern while dealing with real-time datasets in health sector. Privacy preservation algorithms have to ensure protection of sensitive information related to patients’ diagnoses and diseases. Privacy preserving data mining (PPDM) deals with data perturbation, anonymities, and modification as per the requirement of the system. Data perturbation is one of best PPDM techniques that basically deals with numeric values and focuses on privacy implementation. In this chapter, we will select and review different articles that are related to electronic health records (EHRs), their privacy standards, challenges, and regulations currently adopted in different countries. This chapter mainly reviews the current status of privacy preservation polices used in EHR, privacy techniques and analysis, and future scope of privacy in global scenario.

[1]  Maryam Ahmadi,et al.  Security Requirements and Solutions in Electronic Health Records: Lessons Learned from a Comparative Study , 2010, Journal of Medical Systems.

[2]  John Liagouris,et al.  Disassociation for electronic health record privacy , 2014, J. Biomed. Informatics.

[3]  David Sánchez,et al.  Utility-preserving privacy protection of textual healthcare documents , 2014, J. Biomed. Informatics.

[4]  Ming Li,et al.  Securing Personal Health Records in Cloud Computing: Patient-Centric and Fine-Grained Data Access Control in Multi-owner Settings , 2010, SecureComm.

[5]  Yücel Saygin,et al.  Privacy-Preserving Learning Analytics: Challenges and Techniques , 2017, IEEE Transactions on Learning Technologies.

[6]  Abderrahim Beni Hssane,et al.  Big healthcare data: preserving security and privacy , 2018, Journal of Big Data.

[7]  Zarina Shukur,et al.  Security Challenges and Success Factors of Electronic Healthcare System , 2013 .

[8]  José Luis Fernández Alemán,et al.  Security and privacy in electronic health records: A systematic literature review , 2013, J. Biomed. Informatics.

[9]  Vitaly Shmatikov,et al.  The cost of privacy: destruction of data-mining utility in anonymized data publishing , 2008, KDD.

[10]  Mark A Rothstein Health privacy in the electronic age. , 2007, The Journal of legal medicine.

[11]  Jun Pang,et al.  Challenges in eHealth: From Enabling to Enforcing Privacy , 2011, FHIES.

[12]  Philip S. Yu,et al.  Differentially private data release for data mining , 2011, KDD.

[13]  Elisa Bertino,et al.  A privacy preserving assertion based policy language for federation systems , 2007, SACMAT '07.

[14]  Raymond Chi-Wing Wong,et al.  Small sum privacy and large sum utility in data publishing , 2014, J. Biomed. Informatics.

[15]  João P. Vilela,et al.  Privacy-Preserving Data Mining: Methods, Metrics, and Applications , 2017, IEEE Access.

[16]  Stephen E. Fienberg,et al.  Scalable privacy-preserving data sharing methodology for genome-wide association studies , 2014, J. Biomed. Informatics.

[17]  Balamurugan Anandan,et al.  Challenges and Opportunities for Security with Differential Privacy , 2013, ICISS.

[18]  Philip S. Yu,et al.  A General Survey of Privacy-Preserving Data Mining Models and Algorithms , 2008, Privacy-Preserving Data Mining.

[19]  Stéphane M. Meystre,et al.  Text de-identification for privacy protection: A study of its impact on clinical text information content , 2014, J. Biomed. Informatics.

[20]  Reihaneh Safavi-Naini,et al.  Using digital rights management for securing data in a medical research environment , 2010, DRM '10.

[21]  Jiankun Hu,et al.  Corresponding author’s address: , 2022 .

[22]  Samee Ullah Khan,et al.  > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 , 2008 .

[23]  T. Senthil Murugan,et al.  Genetic grey wolf optimization and C-mixture for collaborative data publishing , 2018, Int. J. Model. Simul. Sci. Comput..

[24]  Michael Naehrig,et al.  Private Predictive Analysis on Encrypted Medical Data , 2014, IACR Cryptol. ePrint Arch..

[25]  Jian-Guo Bau,et al.  Secure Dynamic Access Control Scheme of PHR in Cloud Computing , 2012, Journal of Medical Systems.

[26]  Hans-Ulrich Prokosch,et al.  Implementing security and access control mechanisms for an electronic healthcare record , 2002, AMIA.

[27]  Yuguang Fang,et al.  CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring , 2013, IEEE Transactions on Information Forensics and Security.

[28]  Jaideep Vaidya,et al.  Privacy-Preserving SVM Classification on Vertically Partitioned Data , 2006, PAKDD.

[29]  Efthimios Tambouris,et al.  The linked medical data access control framework , 2014, J. Biomed. Informatics.

[30]  Sean M. Randall,et al.  Privacy-preserving record linkage on large real world datasets , 2014, J. Biomed. Informatics.

[31]  Jimeng Sun,et al.  Publishing data from electronic health records while preserving privacy: A survey of algorithms , 2014, J. Biomed. Informatics.

[32]  William J. Buchanan,et al.  DACAR Platform for eHealth Services Cloud , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[33]  Abdul Majeed,et al.  Attribute-centric anonymization scheme for improving user privacy and utility of publishing e-health data , 2019, J. King Saud Univ. Comput. Inf. Sci..

[34]  David Sánchez,et al.  A semantic framework to protect the privacy of electronic health records with non-numerical attributes , 2013, J. Biomed. Informatics.

[35]  Naveed Ahmad,et al.  An efficient privacy mechanism for electronic health records , 2018, Comput. Secur..

[36]  Josep Domingo-Ferrer,et al.  FRR: Fair remote retrieval of outsourced private medical records in electronic health networks , 2014, J. Biomed. Informatics.

[37]  Morteza Zahedi,et al.  Cross-domain graph based similarity measurement of workflows , 2018, J. Big Data.

[38]  Keith Marsolo,et al.  Preparing an annotated gold standard corpus to share with extramural investigators for de-identification research , 2014, J. Biomed. Informatics.

[39]  Ling Liu,et al.  Role-based and time-bound access and management of EHR data , 2014, Secur. Commun. Networks.

[40]  J. Goldman,et al.  Perspective: Virtually Exposed: Privacy And E-Health , 2000 .

[41]  Li-e Wang,et al.  A graph-based multifold model for anonymizing data with attributes of multiple types , 2018, Comput. Secur..

[42]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..