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Table 1. Summary of techniques for understanding users’ privacy preferences, with example studies.

Technique

Scope

Data Protection / Personal Privacy

Principled / Communitarian

Sample sizes

Pros

Cons

Surveys

Data Protection

Neutral

1000-10000

Statistically significant

Probes opinions only

Superficial

Westin

Segmentation

Data Protection

Principled

1000-10000

Simple

GVU

General preferences

Data Protection

Neutral

10000

Historic sequence of studies

Smith et al.

Data protection in organizations

Data Protection

Neutral

<1000

Validated

Not adequate for new technologies

Scenario-based surveys

Individuals’ decisions

Neutral

~100

Realism

Control

Bias

Probes opinions only

Spiekermann

Control in ubicomp

Data Protection

Communitarian

128

Validated

Olson et al.

Two-phased (identify items, then probe prefs)

Personal

Neutral

30-80

Efficient use of participants

Hawkey and Inkpen

Incidental privacy

Personal

Principled

155

ESM / Simulations

Neutral

Neutral

Realism

Immediacy

Cost

Intrusiveness

Consolvo et al.

Location Privacy

Personal

Principled

16

Implausibility

Ammenwerth et al.

Mobile computing

Personal

Neutral

31

Expert feedback

Extensive training

Requires experts

Iachello et al.

Mobile computing

Personal

Communitarian

41

Realism

Immediacy

Cost

Intrusiveness

Focus Groups

Neutral

Neutral

Rich data

Requires experts

Crosstalk

Efficient

Kaasinen

Relation of user with Telecoms

Data Protection

Neutral

13 groups, 3-7 people each

Requires Experts

Hayes

School-based surveillance

Personal

Neutral

4 groups, 4-5 people each

Rich data

Requires Experts

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