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PSEUDONYMIZATION TECHNIQUES FOR PRIVACY STUDY WITH CLINICAL DATA

PSEUDONYMIZATION TECHNIQUES FOR PRIVACY STUDY WITH CLINICAL DATA. YAHAYA ABD RAHIM FAC.INFORMATION AND COMMUNICATION TECHNOLOGY UNIVERSITY TECHNICAL MALAYSIA MALACCA. Introduction. Hospital, clinic or pharmacy among the organizations that huge of personal data.

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PSEUDONYMIZATION TECHNIQUES FOR PRIVACY STUDY WITH CLINICAL DATA

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  1. PSEUDONYMIZATION TECHNIQUES FOR PRIVACY STUDY WITH CLINICAL DATA YAHAYA ABD RAHIM FAC.INFORMATION AND COMMUNICATION TECHNOLOGY UNIVERSITY TECHNICAL MALAYSIA MALACCA

  2. Introduction • Hospital, clinic or pharmacy among the organizations that huge of personal data. • In new trend , Vijay (2002), these organizations are interested to release or publish data for research or public benefit like business or legal reasons. • However most of the data are “SENSITIVE”. • According to Tiangcheng Li & Ninghui Li (2008), many organizations, industries and governments are increasingly publishing and sharing the valuable and sensitive information without to protect of the privacy of entities. Publishing the data may put the respondent’s privacy in risk, GeRuan (2007). • Focus on techniques for data privacy on clinical data.

  3. Introduction • What is Privacy? • Privacy includes the right of individuals and organizations to determine for themselves when, how and to what extent information about them is communicated to others. • What Impact with Hospital or Clinical? • Challenging with managing large data in hospital or clinical especially with legal and ethical.

  4. Literature Review Data Protection Techniques Protection (Data) Purpose : Security & Privacy Application Encrypt Anonymity Source : IHSN ( June 2009) Pseudonymization

  5. Literature Review Issues: Data Privacy Area Privacy (Data) Purpose : Privacy Anonymity in Files & Databases Anonymous communication Anonymous Publication & Storage Anonymous Credentials Anonymous transactions

  6. Literature Review Issues: Data Privacy Medical Application Elements Privacy (Data) Purpose : Privacy Data Flow Segmentation “Hard” de-identification Privacy Risk Assessment Controlled Database Various Types Anonymization

  7. Literature Review Why Data Need To Anonymous? Security (Pure) Incur Problem Researcher (Customize) Publish Pattern / Predict (Customize) • Information Loss • Leak - Privacy Anonymous Process Advertise (Customize)

  8. Literature ReviewIssues : Anonymity Technique • Most anonymous techniques consist in reducing the level of detail in the information provided. Therefore, typically most the result in a loss of information, IHSN (2009). • Difficulties into the role of anonymous as a complete solution to the problem of data protection. It must be considered within the context of the analysis to be done on the data, which information needs to be protect. • Anonymous Process must also be considered within its legal context (Burkhart M., Schatzmann D. & Bernhard P., 2010). But should be the lesser extent for generating licensed files / legal context, IHSN (2009).

  9. Problem Statement • Most anonymous process may cause privacy leakage with the original data from user information. • Chances of loss information in most anonymous process is high.

  10. Scope • The scope of this research are: • Implemented the pseudonymization techniques from anonymous process with medical clinical data. • Using data in offline mode.

  11. Pseudonymization Techniques • always map a given identifier with the same pseudo-ID • map a given identifier with a different pseudo-ID • Time-dependent • location-dependent • content-dependent

  12. Flow On Research Methodology Data Privacy (Domain) Data synthetic Data Reduction Data Perturbation Dataset Pseudonymization Process Anonymous dataset

  13. Pseudonymization Implementations Privacy Protection Data Suppliers (sources) Data Collectors (data registers)

  14. Pseudonymization Implementations: Architecture

  15. Pseudonymization Implementations Data Public PseudonymizationEngine Anonymizer Risk Analyzer Data Storage Source : Enhanced Simplifying Anonymizing Proxy, SaikatGuha, 2011.

  16. Result View (RO4) Black Marker, Truncation Source: Statistic IHSN, 2009 Density Of Information

  17. Conclusion It is expected that this research shall produce: • A new technique in anonymous process which more comprehensive where this technique be reduce or none information loss with protection of privacy leakage.

  18. Future Work • Generalization Process In Pseudonymization • Micro data e.g: Medical data • Network data • OnlineAnonymization Process as Alternative Beside Encryption

  19. end Thank you….. Q & A?

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