Privacy Decision Making in IoT Scenarios Pardis Emami-Naeini , Martin - PowerPoint PPT Presentation

The Influence of Friends and Experts on Privacy Decision Making in IoT Scenarios Pardis Emami-Naeini , Martin Degeling * , Lujo Bauer, Lorrie Cranor, Mohammad Reza Haghighat † , Richard Chow † , Heather Patterson † † *

Alice in Wonderland… Alice’s Phone Data : video Data : fingerprint Data : temperature Retention : 1 month Retention : forever Retention : 1 day Purpose : authentication Purpose : security Purpose : adjust room’s temperature Fewer than 15% of More than 65% of your social influence privacy experts friends allowed allowed 2

Rese esearch qu questio ion What is the impact of social influence on people making privacy-related decisions about allowing data collection by IoT devices? 3

Soc ocia ial l in influence Intentional or unintentional changes to individuals’ opinions or behaviors caused by others. Yes! No! 4

We e stu tudied in indir irect, informatio ional soc socia ial in influence direct indirect normative informational 5

Nor ormativ ive or or in informatio ional soc socia ial in influ luence normative informational 6

We e stu tudied in informatio ional soc socia ial l influence informational 7

Dir irect or or indir irect soc social l in influ luence direct indirect 8

We e stu tudied in indir irect soc social l in influ luence indirect 9

Desi esigned a a vig ignette stu tudy Short hypothetical stories… Once upon a time … 10

Sce cenario ios wher ere be benefit its ou outweig igh risk risks 11

Sce cenario ios wher ere risk risks ou outweigh be benefit its 12

Sce cenario ios wit ith a a ba bala lance 13

Used sed a a pr pre-study to o pi pick sce scenario ios • 500 Mechanical Turk participants • From the United States • Presented with 28 hypothetical IoT data-collection scenarios • Asked whether participants would allow data collection • Compensated for $2.50 14

Sele elected 9 pr pre-study sce scenario ios 3 allow more than 80% allowed 3 deny fewer than 20% allowed 45% to 55% allowed 3 balanced 15

Con onsis istency Alice’s Phone Data : temperature Retention : 1 day Purpose : adjust room’s Allow! temperature Pilot Participants More than 85% of privacy experts Consistent allowed 16

Con onsis istency Alice’s Phone Data : temperature Retention : 1 day Purpose : adjust room’s Allow! temperature Pilot Participants Fewer than 15% of privacy experts Inconsistent allowed 17

Two o con onsensus le level l for or soc socia ial cue cues strong • “More than 85% of [influencer] allowed the data collection.” • “Fewer than 15% of [influencer] allowed the data collection.” weak • “More than 65% of [influencer] allowed the data collection.” • “Fewer than 35% of [influencer] allowed the data collection.” 18

5 stu tudy con ondit itions inconsistent inconsistent consistent consistent consistent consistent control inconsistent inconsistent friends experts friends condition experts • Out of 9 scenarios in each experimental condition • 5 strong social cues • 4 weak social cues 19

Ex Exam ample le of of a a bal balanced sce scenario io You are at the library. This message is displayed on your smartphone: • Your smartwatch is keeping track of your specific position. • Your position is used by the smartwatch to determine possible escape routes in the case of an emergency. • This data will never be deleted. • [Experimental conditions] Fewer than 35% of your friends allowed this data collection. 20

1000 Mec echanic ical Tur urk par partic icipants • From the United States • 200 participants per condition • Avg. age: 35 • ~15 minutes to complete • Compensated for $2.50 21

Que uestio ions pe per sce scenario io • If you had the choice, would you allow or deny this data collection? inferred impact of social influence 22

Que uestio ions aft fter 9 all all sce scenarios • When considering the 9 scenarios above, how much were you influenced by the decisions that [influencer] made in these scenarios? self-reported impact of social influence 23

Used sed reg egressio ion to o ana analy lyze • Applied GLMM + random intercept • Model selection by backward elimination 24

Soc ocia ial l in influence e mak akes a a dif difference! • People are influenced by privacy experts and their friends differently • Example: 11% more allowed in the “allow” scenarios when influenced by consistent experts, compared to control condition with no influence • Social influence speeds up decision making Sure! 25

Soc ocia ial l in influence e spe speeds up up de decisio ion mak akin ing • In general among all conditions: • allow < deny < balanced • Impact of social influence: • With social influence (3.69 s) < without social influence (4.24 s) • Biggest impact on balanced scenarios: • With social influence (3.61 s) < without social influence (4.55 s) 26

Fac actors im impactin ing the the ext xten ent t of of soc socia ial l in influence • Task difficulty • Most influence in balanced scenarios • Consistency • Consistent social cues have more influence • Strength of social cues • Strength of cues directly relates to the influence • Type of influencer • Experts allow • Friends deny 27

Reported to o be sign ignificantly more in influenced by con onsistent frie friends th than by in inconsistent frie friends Influenced to follow Influenced to oppose the cue the cue inconsistent social cue not influenced 0 50 100 150 200 number of participants 28

Reported to o be sign ignificantly more in influenced by con onsistent frie friends th than by in inconsistent frie friends Influenced to follow Influenced to oppose the cue the cue inconsistent social cue not influenced consistent social cue not influenced 0 50 100 150 200 number of participants 29

Reported to o be sign ignificantly more in influenced by con onsistent exp xperts th than by in inconsistent experts Influenced to follow Influenced to oppose the cue the cue inconsistent social cue not influenced not influenced consistent social cue not influenced 0 50 100 150 200 number of participants number of participants 30

Peo eople le rep eported to o pr prefer in influence fr from om exp xper erts • Reported to be significantly more influenced when being asked about privacy experts in control condition Influenced to follow Influenced to oppose the cue the cue cue comes from friends not influenced 0 50 100 150 200 number of participants 31

Peo eople le rep eported to o pr prefer in influence fr from om exp xper erts • Reported to be significantly more influenced when being asked about privacy experts in control condition • Most mentioned quality to be influenced by: having background in technology Influenced to oppose Influenced to follow the cue the cue cue comes from friends not influenced not influenced cue comes from experts not influenced 0 50 100 150 200 number of participants 32

Soc ocia ial l in influence e in in act actio ion • Social influence is a promising approach for privacy assistants • Important to choose influencers carefully and evaluate them over time More info: www.privacyassistant.org Pardis Emami-Naeini , Martin Degeling, Lujo Bauer, Lorrie Cranor, Mohammad Reza Haghighat, Richard Chow, Heather Patterson 33