The document discusses de-identification and the De-identification Maturity Model (DMM). The DMM is a framework that evaluates an organization's maturity in de-identifying data based on their people, processes, technologies, and measurement practices. It assesses an organization across three dimensions: practice, implementation, and automation. Higher levels of maturity indicate more robust de-identification processes that better balance privacy and data utility. The document provides examples of how the DMM could be used to evaluate different organizations' de-identification practices.

1. Big Data Meets Privacy:
De-identification Maturity Model for Benchmarking and
Improving De-identification Practices
Nathalie Holmes
Khaled El Emam

2. Workshop Outline
 Big Data: Opportunities and Risks in Healthcare
 De-identification Myths: Fact or Fiction
 Overview of Terms Used in Anonymization
 De-identification Maturity Model (DMM) Case
Studies
 DMM Uses and Benefits

3. OPPORTUNITIES AND RISKS WITH BIG DATA
How to Successfully Leverage Data
While Protecting Individual Privacy

4. Big Data Tidal Wave is Creating Unforeseen
Opportunities and Risks

5. Organizations with the Right Tools
And a Skilled Team will
Come Out on Top

6. Big Data Opportunities and Risks
 A lot of useful data contains personal information about patients, study
participants, or consumers
 The challenge is getting access to the data – addressing the privacy
requirements:
- Do you have authority ?
- Is it mandatory or discretionary ?
- Do you patient / participant consent ?
- Can you anonymize the data
 These are the only ways that you get access to the data

7. Healthcare Breaches
 Best evidence suggests at least 27% of healthcare practices have a
breach every year
 The costs for healthcare are $200 per individual for breach notification
(Ponemon)
 This applies whether you have obtained consent or authority

8. De-identification is one piece of an enterprise privacy
program that can make privacy work
“Privacy by Design” provides helpful best practices
Proactive, Preventative, Embedded and Continuous

9. De-Identification Facts or Fiction #1
 True or False:
- It’s possible to re-identify most, if not all, data.
 False:
- Using robust methods, evidence suggests risk
can be very small.

10. De-Identification Facts or Fiction #2
 True or False:
- Privacy regulations say that there must be zero
chance of re-identification in order for a data set
to be used for secondary purposes.
 False:
- HIPAA states that the risk of re-identification
must be “very small”. The FTC and other
regulations use a “reasonableness” standard. All
of these standards take context into account

11. De-Identification Facts or Fiction #3
 True or False:
- Only covered entities should consider HIPAA as
a standard for de-identification.
 False:
- HIPAA is a good standard to use regardless of
the applicable regulations.

12. OVERVIEW OF ANONYMIZATION
How to Successfully Leverage Data
While Protecting Individual Privacy

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Balancing Data Privacy Requires Evaluation
of Privacy Protection and Data Utility

14. Balancing Data Privacy

15. Direct and In-Direct/Quasi-Identifiers
Examples of direct identifiers: Name, address, telephone
number, fax number, MRN, health card number, health plan
beneficiary number, license plate number, email address,
photograph, biometrics, SSN, SIN, implanted device number
Examples of quasi identifiers: sex, date of birth or age,
geographic locations (such as postal codes, census
geography, information about proximity to known or unique
landmarks), language spoken at home, ethnic origin, total
years of schooling, marital status, criminal history, total income,
visible minority status, profession, event dates

16. Terminology

17. A process that removes the association
between the identifying data and the data
subject. (Source ISO/TS 25237:2008)

18. Reducing the risk of identifying a data
subject to a very small level through the
application of a set of data transformation
techniques without any concern for the
analytics utility of the data.

19. Removal of fields
from a data set

20. A particular type of anonymization that both
removes the association with a data
subject and adds an association between a
particular set of characteristics to the data
subject and one or more pseudonyms
(Source: ISO/TS 25237:2008)

21. Replacing a value in
the data with a random
value from a large
database of possible
values

22. Data Masking
Data Masking =
No analytics on those
fields

23. Reducing the risk of identifying a data subject to
a very small level through the application of a set
of data transformation techniques such that the
resulting data retains a very high analytics value.

24. Reducing the
precision of a value
to a more general
one

25. The removal of
records or values
(cells) in the data

26. Randomly selecting a subset of records or
patients from a data set

27. The motives and
capacity of the
data recipient to
re-identify the data

28. The security and
privacy practices
that the data
recipient has in
place to manage
the data received.

29. Statistical De-identification
De-identification =
High analytical value

30. RE-IDENTIFICATION RISKS
Risks from Basic Demographics

37. DE-IDENTIFICATION MATURITY MODEL
How to Successfully Leverage Data
While Protecting Individual Privacy

38. De-identification Maturity Model (DMM)
 Formal framework to evaluate maturity of de-identification services
within an organization
 Gauges level of an organization’s readiness and experience in
relation to people, processes, technologies and consistent
measurement practices
 “DMM” used as a measurement tool; enables the enterprise to
implement a grounded strategy based on facts
 Improves compliance, facilitates access, and scales support services

39. Three Dimensions of the DMM
A
CB

40. Practice Dimension
 DMM has five maturity levels for the de-identification practices
that an organization has in place
 Level 1 is lowest level of maturity and level 5 is the highest
level of maturity
Adhoc Masking Heuristic
Risk
Based
Governance
1 2 3 4 5
A

41. Case Study 1 – Safe Harbor
 Organization A is a disease registry
 They have lots of databases that they connect to and they do a lot of
data releases to internal and external data analysts
 Practice Dimension (what you do):
- Their primary way of anonymizing data is through following the Safe
Harbor de-identification standard (L3)
 Implementation Dimension (how well you do it):
- There is a clear process and well defined roles for following SH,
which is well documented
- Because its documented, it’s repeatable (L3)

42. Safe Harbor
Safe Harbor Direct Identifiers and Quasi-identifiers
1. Names
2. ZIP Codes (except first
three)
3. All elements of dates
(except year)
4. Telephone numbers
5. Fax numbers
6. Electronic mail
addresses
7. Social security
numbers
8. Medical record
numbers
9. Health plan beneficiary
numbers
10.Account numbers
11.Certificate/license
numbers
12.Vehicle identifiers and
serial numbers,
including license plate
numbers
13.Device identifiers and
serial numbers
14.Web Universal
Resource Locators
(URLs)
15.Internet Protocol (IP)
address numbers
16.Biometric identifiers,
including finger and
voice prints
17.Full face photographic
images and any
comparable images;
18. Any other unique
identifying number,
characteristic, or code
Actual Knowledge

43. Case Study 1 – Safe Harbor
 Automation dimension (is it automated)
- They use a home grown scripts for implementing SH
- The scripts do not have any external validation that they work or are
sufficient (L1)
 Challenges
- Despite these efforts, they have missed some key items
- There have been pressures by analysts to provide more granular
data

44. Case Study 1 – Safe Harbor
- They have interpreted the SH regulation for dates such that they
have only dealt with dates of birth rather than all dates
- They have not brought all zip down to 3, and for regions where there
are fewer than 20K people replace with 000 per SH
- Some identifiers were missed (such as clinical trial participant
numbers)
- Did not consider the Actual Knowledge requirement in SH

45. Case Study 2 – Masking
 Company B is a claims processor
 They have a need for realistic data for software testing
 Practice Dimension (what you do):
- Their primary way of anonymizing is through data masking
- This means they deal only with the direct identifiers (L2)
 Implementation Dimension (how well you do it):
- There is a clear process for doing masking and how they implement
heuristics, which is well documented
- Because its documented, it’s repeatable (L3)

46. Case Study 2 – Masking
 Automation dimension (is it automated)
- They use a commercial product for masking
- This product produces consistent results (L2)
 Challenges
- Despite these efforts, they have missed some key items – the quasi-
identifiers
- Some dates and ZIP codes were not addressed
- There is no evidence that the risk of re-identification was “very small”
- The tool vendor architect provided assurance that this was OK

47. Case Study 3 – Governance
 Company C is an EMR vendor
 They have a need to provide reports to their clients on trends and
benchmarks to help clients to improve their businesses
 Practice Dimension (what you do):
- They have a risk-based approach which includes anonymizing both
direct identifiers (masking) and in-direct identifiers (de-identification)
 Implementation Dimension (how well you do it):
- There is a clear process for anonymizing the data which is well
documented
- Because its documented, it’s repeatable

48. Case Study 3 – Governance
- They have on-going training of staff on how to do the
anonymization
- They are able to quickly produce reports and metrics
documenting what they did to the data before they released it
- They have automated data sharing agreements which specifies
the controls that need to be in place by data users
- They have a full audit trail to demonstrate that the risk of re-
identification is “very small” per HIPAA
- They track when there is overlap between the various data sets
- Audits are conducted on data users to confirm compliance with
conditions

49. Case Study 3 – Governance
 Automation Dimension (is it automated)
- They use commercial software to do masking and de-
identification
- The product produces consistent results
- They are able to get defensible anonymization more quickly than
by doing it manually
- The product has been scrutinized by other users & peers and is
upgraded on a regular basis
- They are able to release more data sets, more quickly

50. Benefits of DMM
 Determine whether an organization can defensibly ensure risk of re-
identification is “very small”
 Provides a road map to meet regulatory and legal requirements
 Automation and governance allow organizations to share more data for
secondary purposes with fewer resources
 A higher the level of maturity results in higher quality data and greater
consistency in de-identification
 Significant improvement in ability to estimate resources and time
required to de-identify data sets

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Key Learnings

52. Data Anonymization Resources
Book Signing:
Sept 26,10:35 am Booth # 107
Khaled El Emam & Luk Arbuckle

53. Other Conference Activities
 Session: Facilitating Analytics While Protecting Individual Privacy Using
Data De-identification - Khaled El Emam
- Thursday , September 26 @ 4:00pm, Salon F
 Office hours in the Sponsor Pavilion:
- Nathalie Holmes - Thursday, September 26 @ 3:10pm, Table D
- Khaled El Emam - Thursday, September 26 @ 6:30pm, Table D

54. Contact
Nathalie Holmes:
nholmes@privacyanalytics.ca
613.369.4313 ext 122
Khaled El Emam:
kelemam@ehealthinformation.ca
613.738.4181
@PrivacyAnalytic
2012 Start-Up Showcase Winner

55. Review Quiz
 What does anonymization mean?
 What is the difference between data masking and de-identification?
 Why is it important to strive for balance between privacy and data utility?
 How many levels of maturity (Practice Dimension) are there in the DMM?
 Is it possible to be at Practice Dimension 1 (Ad hoc) and score well in the
Implementation Dimension? Ex. Have a repeatable, defined and measurable
process?
 What are some advantages of having Standard Automation (software)?
 What is the main difference between Practice Dimension 4 (Risk Based) and
Dimension 5 (Governance)?