De-Identification Handbook
2.0.0-comment - ballot

De-Identification Handbook, published by IHE IT Infrastructure Technical Committee. This guide is not an authorized publication; it is the continuous build for version 2.0.0-comment built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/IHE/ITI.DeIdHandbook/ and changes regularly. See the Directory of published versions

## References

1. ISO 25237. (2017). Health informatics — Pseudonymization (ISO 25237:2017; Number ISO 25237:2017). International Organization for Standardization. https://www.iso.org/standard/63553.html
2. ISO/IEC 20889. (2018). Privacy enhancing data de-identification terminology and classification of techniques (Standard ISO/IEC 20889:2018(E); Number ISO/IEC 20889:2018(E)). International Organization for Standardization. https://www.iso.org/standard/69373.html
3. GDPR. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council. European Parliament and Council of the European Union. https://data.europa.eu/eli/reg/2016/679/oj
4. PIPL. (2021). Personal Information Protection Law of the People’s Republic of China. National People’s Congress of the People’s Republic of China (NPC). http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm
5. NIST 800-188. (2023). De-identifying Government Datasets (Special Publication No. 800-188; Numbers 800-188). National Institute of Standards and Technology. https://doi.org/10.6028/nist.sp.800-188
6. European Data Protection Board. (2025). Guidelines 01/2025 on Pseudonymisation. https://www.edpb.europa.eu/system/files/2025-01/edpb_guidelines_202501_pseudonymisation_en.pdf
7. Information Commissioner’s Office. (2025). Pseudonymisation. Information Commissioner’s Office. https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/anonymisation/pseudonymisation/#pseudonymiseddatastillpersonal
8. Office for Civil Rights. (2025). Methods for De-identification of PHI. HHS.gov. https://www.hhs.gov/hipaa/for-professionals/special-topics/de-identification/index.html#rationale
9. Hintze, M. (2017). Viewing the GDPR through a de-identification lens: a tool for compliance, clarification, and consistency. International Data Privacy Law, 8(1), 86–101. https://doi.org/10.1093/IDPL/IPX020
10. U.S. Congress. (1996). Health Insurance Portability and Accountability Act of 1996. Public Law 104-191. https://www.govinfo.gov/content/pkg/PLAW-104publ191/pdf/PLAW-104publ191.pdf
11. GB/T 42460. (2023). Information security technology - Guide for evaluating the effectiveness of personal information de-identification (Standard GB/T 42460—2023; Number GB/T 42460—2023). State Administration for Market Regulation Standardization Administration of China. https://www.iso.org/standard/69373.html
12. University of Manchester. (2024). Anonymisation decision-making framework: European practitioners’ guide (2nd ed.). University of Manchester. https://ukanon.net/wp-content/uploads/2024/01/adf-2nd-edition-european-practitioners-guide-final-version-cover-2024-version-2.pdf
13. National Electrical Manufacturers Association. (2025). DICOM Part 15, Annex E: Security and System Management Profiles — Attribute Confidentiality Profiles (PS3.15 Annex E; Number PS3.15 Annex E). National Electrical Manufacturers Association. https://dicom.nema.org/medical/dicom/current/output/chtml/part15/chapter_E.html
14. ISO/IEC 27559. (2022). Information security, cybersecurity and privacy protection — Privacy enhancing data de-identification framework (ISO/IEC 27559:2022; Number ISO/IEC 27559:2022). International Organization for Standardization. https://www.iso.org/standard/71677.html
15. ISO 27799. (2025). Health informatics — Information security management in health using ISO/IEC 27002. International Organization for Standardization. https://www.iso.org/standard/86754.html
16. ISO/TS 22220. (2011). Health informatics — Identification of subjects of care (ISO/TS 22220:2011; Number ISO/TS 22220:2011). International Organization for Standardization. https://www.iso.org/standard/44439.html
17. Article 29 Data Protection Working Party. (2014, April 10). Opinion 05/2014 on anonymisation techniques (WP216). European Commission. https://ec.europa.eu/justice/article-29/documentation/opinion-recommendation/files/2014/wp216_en.pdf
18. HP. Efficient signature schemes supporting redaction, pseudonymization, and data deidentification. HP. HPL-2007-191
19. Schneier, Bruce. Commentary on the Importance of a Systemic Approach to Security. Bruce Schneier. Essay-028
20. UK Redaction Toolkit. A United Kingdom government document describing a toolkit for removing content prior publication for various legal reasons. REDACTION GUIDELINES FOR THE EDITING OF EXEMPT INFORMATION FROM PAPER AND ELECTRONIC DOCUMENTS PRIOR TO RELEASE
21. DICOM. DICOM current publication
22. DICOM Part 15 Annex E. Discusses clinical trials, double-blinding, traceability (relinking) to original content, preserving data needed for the trial. DICOM Part 15, Annex E current publication
23. Moehrke. De-Identification is Highly Contextual. (2009) De-Identification is highly contextual
24. HITSP. HITSP Biosurveillance Use Case Presentation. Lists summary of units of data exchange and values that should be pseudonymized. HITSP Biosurveillance Use Case presentation
25. NIH Biosurveillance Test Case Scenarios. Presentation by David Dobbs on the Biosurveillance Use Case and Minimum Data Elements
26. NHN Biosurveillance Test Scenarios. NHN Testing Work Group document *Biosurveillance Test Case Scenarios. 2008-10-03.xls* file, draft.
27. Dresden Anonymity. A proposed vocabulary for pseudonymization and related concepts. Anonymity, Unlinkability, Undetectability, Unobservability, Pseudonymity, and Identity Management – A onsolidated Proposal for Terminology* (Version v0.31 Feb. 15, 2008)
28. MIT Reidentification. Shows that a large percentage of people can be re-identified with Date-of-Birth, Current ZIP Code, and Sex. Reidentification of Individuals in Chicago's Homicide Database A Technical and Legal Study
29. European Medicines Agency. (2025, May 14). External guidance on the implementation of the European Medicines Agency Policy 0070 on the publication of clinical data for medicinal products for human use (Version 1.5). https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/external-guidance-implementation-european-medicines-agency-policy-publication-clinical-data-medicinal-products-human-use-version-15_en.pdf
30. Information and Privacy Commissioner of Ontario. (2016, June). De-identification Guidelines for Structured Data. https://www.ipc.on.ca/sites/default/files/legacy/2016/08/Deidentification-Guidelines-for-Structured-Data.pdf
31. Branson, J., Good, N., Chen, JW. et al. (2020). Evaluating the re-identification risk of a clinical study report anonymized under EMA Policy 0070 and Health Canada Regulations. Trials 21, 200. https://doi.org/10.1186/s13063-020-4120-y
32. Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 557-570. https://doi.org/10.1142/s0218488502001648
33. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3-4), 211-407. https://doi.org/10.1561/0400000042
34. El Emam, K. (2013). Guide to the de-identification of personal health information. Auerbach Publications. https://doi.org/10.1201/b14754
35. Shahid, A., Bazargani, M. H., Banahan, P., Mac Namee, B., Kechadi, T., Treacy, C., Regan, G., & MacMahon, P. (2022). A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis. Healthcare (Basel, Switzerland), 10(5), 755. https://doi.org/10.3390/healthcare10050755
36. Mayer, D. A., Teubert, D., Wetzel, S., & Meyer, U. (2011). Implementation and performance evaluation of privacy-preserving fair reconciliation protocols on ordered sets. In Proceedings of the First ACM Conference on Data and Application Security and Privacy (pp. 109–120). Association for Computing Machinery. https://doi.org/10.1145/1943513.1943529
37. Domingo-Ferrer, J., & Mateo-Sanz, J. M. (2002). Practical data-oriented microaggregation for statistical disclosure control. IEEE Transactions on Knowledge and Data Engineering, 14(1), 189–201. https://doi.org/10.1109/69.979982
38. Poulis, G., Loukides, G., Gkoulalas-Divanis, A., & Skiadopoulos, S. (2013). Anonymizing Data with Relational and Transaction Attributes. ECML/PKDD.
39. Liu, K., & Terzi, E. (2008). Towards identity anonymization on graphs. Proceedings of the 2008 ACM SIGMOD international conference on Management of data.
40. NIST. (2024). Guidelines for Evaluating Differential Privacy Guarantees (Special Publication No. 800-226). National Institute of Standards and Technology. https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-226.pdf
41. Future of Privacy Forum. (2020, April). A practical path toward genetic privacy. https://fpf.org/wp-content/uploads/2020/04/APracticalPathTowardGeneticPrivacy_April2020.pdf
42. IHE International. (2025). IHE radiology technical framework, volume 1: Integration profiles (Rev. 23.0). https://www.ihe.net/uploadedFiles/Documents/Radiology/IHE_RAD_TF_Vol1.pdf
43. O'Keefe, C., Otorepec, S., Elliot, M., Mackey, E., & O'Hara, K. (2017). The de-identification decision-making framework. CSIRO Data61. https://doi.org/10.4225/08/59c169433efd4
44. Information and Privacy Commissioner of Ontario. (2016). De-identification guidelines for structured data. https://www.ipc.on.ca/sites/default/files/legacy/2016/08/Deidentification-Guidelines-for-Structured-Data.pdf
45. Jiang, Y., Mosquera, L., Jiang, B., Kong, L., & El Emam, K. (2022). Measuring re-identification risk using a synthetic estimator to enable data sharing. PLOS ONE, 17(6), e0269097. https://doi.org/10.1371/journal.pone.0269097