This document discusses the intersection of privacy considerations and data science, emphasizing the importance of understanding data usage and ensuring compliance with privacy legislation. It highlights case studies from companies like Target and Netflix, and outlines best practices for implementing privacy by design in data management. The author calls for organizations to be proactive in auditing their data practices and engaging with privacy experts to mitigate potential privacy violations.

1. PRIVACY CONSIDERATIONS
WITH DATA AND DATA SCIENCE
David Stephenson, Ph.D.
dsiAnalytics.com

2. PRIVACY CONSIDERATIONS WITH DATA AND DATA SCIENCE
• Intro & Case Studies
• Data & Data Science: Growth and Usage
• Privacy: Storm on the Horizon
• Concluding Thoughts
Agenda
2

3. My Background
Intro & Case Studies
Head of Global Business
Analytics
Professor
(Advanced Analytics)
Ph.D. Analytics &
Computer Science
Financial Analytics,
Credit Risk and Insurance
Independent Consultant
3

4. Target corporation makes an embarrassing revelation
Intro & Case Studies
Legal
4

5. Netflix Plays with Fire and Gets Burned
Intro & Case Studies
5
Netflix
5

6. PRIVACY CONSIDERATIONS WITH DATA AND DATA SCIENCE
• Intro & Case Studies
• Data & Data Science: Growth and Usage
– Data Science
– Modern Technology
• Privacy: Storm on the Horizon
Agenda
6

7. Data Usage
The Power of Data Science
Propensity Classification/Profiling
PersonalizationMarketing
7

8. More Data Means More Insights
Traditional Data
Big
Data
Smart
Devices
IoT
8

9. William Weld
Healthcare Case Study: Linking Data Destroys Anonymization
Data Science
9

10. PRIVACY CONSIDERATIONS WITH DATA AND DATA SCIENCE
• Intro & Case Studies
• Data & Data Science: Growth and Usage
– Data Science
– Modern Technology
• Privacy: Storm on the Horizon
Agenda
10

11. Data Sources
The Power of Data Science
11
Brainstorm: Today’s Sources of Personal Data? 11

12. Sources: Browsing
Data Growth: Modern Technology
12

13. Sources: off-line behavior
Data Growth: Modern Technology
13

14. Sources: Biometrics
Data Growth: Modern Technology
14

15. Sources: The Internet of Things
Data Growth: Modern Technology
15

16. Data Storage
Data Growth: Modern Technology
16

17. Source and Use of Customer Data
Privacy: A Brief Background
Can be
Known
Used
Stored
Shared with
3rd parties
Observed
Volunteered
Data
Science
17

18. PRIVACY CONSIDERATIONS WITH DATA AND DATA SCIENCE
18
• Intro & Case Studies
• Data & Data Science: Growth and Usage
• Privacy: Storm on the Horizon
Agenda

19. Privacy Legislation
19
Privacy Legislation
EU Data
Protection
Directives
19

20. Preparing for compliance
Privacy: Storm on the Horizon
What are my
data assets?
Usage
Storage
Flow to/from
3rd parties
Observed
Volunteered
Data
Science
Right to be forgotten
De-anonymization
Cloud computing
Explicit and up-
front consent
Restricted profiling
Privacy by Design
Potential liabilities from
buying, selling and sharing
20

21. Moving Forward
Privacy: Storm on the Horizon
ü Become aware of your entire data ecosystem and how it may expose
you to privacy violations
ü Audit current data storage and governance for compliance
ü Ensure that all product roadmaps comply with the principles of
Privacy by Design
21
ü Ensure that proper user consent is in place from the moment of first
user registration
ü Initiate dialogue with corporate privacy officer or external expert

22. 22
david@dsiAnalytics.com
@DataScienceInno
Contact

23. Appendix
23

24. Privacy by Design
24
1 Proactive not Reactive; Preventative not Remedial
2 Privacy as the Default Setting
3 Privacy Embedded into Design
4 Full Functionality – Positive-Sum, not Zero-Sum
5 End-to-End Security – Full Lifecycle Protection
6 Visibility and Transparency – Keep it Open
7 Respect for User Privacy – Keep it User-Centric

25. Privacy by Design for Big Data (Jeff Jonas, IBM)
25
1. FULL ATTRIBUTION: Every observation (record) needs to know from where it came and when. There cannot be merge/
purge data survivorship processing whereby some observations or fields are discarded.
2. DATA TETHERING: Adds, changes and deletes occurring in systems of record must be accounted for, in real time, in sub-
seconds.
3. ANALYTICS ON ANONYMIZED DATA: The ability to perform advanced analytics (including some fuzzy matching) over
cryptographically altered data means organizations can anonymize more data before information sharing.
4. TAMPER-RESISTANT AUDIT LOGS: Every user search should be logged in a tamper-resistant manner — even the
database administrator should not be able to alter the evidence contained in this audit log.
5. FALSE NEGATIVE FAVORING METHODS: The capability to more strongly favor false negatives is of critical importance
in systems that could be used to affect someone’s civil liberties.
6. SELF-CORRECTING FALSE POSITIVES: With every new data point presented, prior assertions are re-evaluated to
ensure they are still correct, and if no longer correct, these earlier assertions can often be repaired — in real time.
7. INFORMATION TRANSFER ACCOUNTING: Every secondary transfer of data, whether to human eyeball or a tertiary
system, can be recorded to allow stakeholders (e.g., data custodians or the consumers themselves) to understand how
their data is flowing.