Presentation at FOX MIS Distinguished Speakers Series at Fox School of Business, Temple University (March 19, 2021).

1. Impact of End-user PETs
on Firms’ Analytics Performance
Jungpil Hahn
and
Dawei Chen

2. Impact of End-user PETs
on Firms’ Analytics Performance
Jungpil Hahn
and
Dawei Chen

3. https://www.flickr.com/photos/jamiedfw

4. <1>

6. https://www.flickr.com/photos/akbarsyah

8. </1>

9. <2>

10. https://www.flickr.com/photos/bookcatalog/

11. https://www.flickr.com/photos/djandywdotcom/

12. https://www.flickr.com/photos/53887959@N07
PETs

13. PETs
Privacy Enhancing Technologies

14. </2>

15. <1 + 2>
Data Analytics
Value
Data Integrity
Consumer
adoption of
PETs
(-) (-)

16. Toward a Framework of End-User PETs
• What are the impacts of PETs on firms’ analytics capabilities /
performance?
• Should firms be concerned? If so, at what point?
• What is the value of consumer data?
• How should firms adapt?
• …

17. What kinds of PETs are out there?

18. Classifying End-User PETs
• Literature
• Communication anonymizers
• Privacy settings
• Transparency enhancing
technologies
• Trackers / evidence erasers
• Filters / blockers
• Personal data stores
• Practice
• Communication anonymizers (45%)
• Filters / blockers (28%)
• Trackers / evidence erasers (14%)
• Permission managers (8%)
• Privacy checkers (4%)
• Privacy education (1%)

19. Classifying End-User PETs
• Literature
• Communication anonymizers
• Privacy settings
• Transparency enhancing
technologies
• Trackers / evidence erasers
• Filters / blockers
• Personal data stores
• Low-tech lying
• Practice
• Communication anonymizers (45%)
• Filters / blockers (28%)
• Trackers / evidence erasers (14%)
• Permission managers (8%)
• Privacy checkers (4%)
• Privacy education (1%)

20. Can we abstract/generalize?

21. Theoretical Framework of End-User PETs
Measurement Error MissingValue
Entities
Attributes
Relationships

22. Theoretical Framework of End-User PETs
Measurement Error MissingValue
Entities Communication Anonymizers
Fake accounts
Privacy Settings
Personal Data Store
Attributes Shared bogus account Transparency EnhancingTech
Tracker / Erasers
Relationships Shared bogus account Transparency EnhancingTech
Tracker / Erasers

23. When should firms be concerned?

25. Ratings
Data
Recommendation
Algorithm
PET-tainted
Ratings
Data
Recommendation
Algorithm
Recommendation
Performance
Recommendation
Performance

26. Ratings
Data
Recommendation
Algorithm
PET-tainted
Ratings
Data
Recommendation
Algorithm
Recommendation
Performance
Recommendation
Performance
PET Impact

27. Ratings
Data
Recommendation
Algorithm
PET-tainted
Ratings
Data
Recommendation
Algorithm
Recommendation
Performance
Recommendation
Performance
PET Impact
Contingency Factors

28. Contingency Factors
• User characteristics
• Adoption Rate
• Adoption Pattern
• PET characteristics
• Protection Mechanism
• Protection Intensity

29. Contingency Factors
• User characteristics
• Adoption Rate
• 0 ~ 90%
• Adoption Pattern
• Uniform
• Heavy-sensitive
• Light-sensitive
• PET characteristics
• Protection Mechanism
• Missing value
• Measurement error
• Protection Intensity
• 0 ~ 90% of adopter’s data

30. Contingency Factors
• User characteristics
• Adoption Rate
• 0 ~ 90%
• Adoption Pattern
• Uniform
• Heavy-sensitive
• Light-sensitive
• PET characteristics
• Protection Mechanism
• Missing value
• Measurement error
• Protection Intensity
• 0 ~ 90% of adopter’s data

31. Missing
Value
Measurement
Error
Uniform Heavy-sensitive Light-sensitive

32. MV vs. ME Uniform Heavy-sensitive Light-sensitive
Item-based CF 4.208 7.835 5.976
User-based CF -9.801 -10.779 -8.759
CF Algorithm?

33. Discussions / Conclusions
• Theoretical framework of end-user PETs from a business analytics
perspective
• Walkthrough of how this framework can be used to assess impact of
PETs on firms’ analytics performance

34. Limitations
• …
• …
• …
• …
• …
• …
• Too many to list J