Mining Privacy Settings to Find Optimal Privacy-Utility Tradeoffs for Social Network Services

1. Mining Privacy Settings to Find Optimal Privacy-Utility Tradeoffs for Social Network Services ShuminGuo,Keke Chen Data Intensive Analysis and Computing (DIAC) Lab Kno.e.sis Center Wright State University

2. Outline • Introduction • Background • Research goals • Contributions • Our modeling methods • The IRT model • Our research hypothesis • Modeling social network privacy and utility • The weighted/personalized utility model • Trade off between privacy and utility • The experiments • Social network data from Facebook • Experimental Results • Conclusions

3. Introduction

4. Background • Social network services (SNS) are popular • SNS are filled up with private info  privacy risks • Online identity theft • Insurance discrimination • … • Protecting SNS privacy is complicated • Many new young users • Do not realize privacy risks • Do not know how to protect their privacy • Privacy settings • consist of tens of options • involve implicit privacy-utility tradeoff A privacy guidance for new young users?

5. Some facts • Privacy settings of Facebook • 27 items • Each item is set to one of the four levels of exposure (“me only”, “friends only”, “friends of friends”, “everyone”) • By default, most items are set to the highest exposure level • the best interest to the SNS provider is to get people exposed and connected to each other

6. Research goals • Understand the SNS privacy problem • The level of “privacy sensitivity” for each personal item • Quantification of privacy • The balance between privacy and SNS utility • Enhancement of SNS privacy • How to help users express their privacy concerns? • How to help users automate the privacy configuration with utility preference in mind?

7. Our contributions • Develop a privacy quantification framework that considers both privacy and utility • Understand common users’ privacy concerns • Help users achieve optimal privacy settings based on their utility preferences • We study the framework with real data obtained from Facebook

8. Modeling SNS Users’ Privacy Concerns

9. Basic idea • Use the Item Response Theory (IRT) model to understand existing SNS users’ privacy settings • Derive the quantification of privacy concern with the privacy IRT model • Map a new user’s privacy concern to the IRT model  find the best privacy setting

10. The Item Response Theory(IRT) model • A classic model used in standard test evaluation • Example, estimate the ability level of an examinee based on his/her answers to the a number of questions

11. The two-parametric model • α  level of discrimination for a certain question • β Level of difficulty for a certain question • θ  Level of a person’s certain trait

12. Mapping to privacy problem • Question answer  profile item setting • Ability level of privacy concern • Beta  sensitivity of profile item • Alpha  contribution to overall privacy concern

13. What we get… relationships Probability of hiding the item network Current_city Level of privacy concern 

14. Our Research Approach • Observation: Users disclose some profile items while hide others • If a user believes an item is not too sensitive, he/she will disclose this item • If a user perceives an item as critical to realize his/her social utility, he/she may also disclose it • Otherwise, user will hide this item • Hypothesis: Users have some implicit balance judgment behind their SNS activities • If utility gain > privacy risk  disclose • If utility gain < privacy risk  hide

15. Modeling SNS privacy • Use the two-parametric IRT model • New interpretation of the IRT model α  profile-item weight for a user’s overall privacy concern β Sensitivity level of the profile item θ  Level of a user’s privacy concern

16. The complete result looks like…

17. Finding optimal settings • Theorem: Privacy rating at i User settings for items: 1: hidden, 0: disclosed Probability of hiding the item

18. Modeling SNS utility – the same method • λ profile-item weight for a user’s SNS utility • μ importance level of the profile item • φ Level of a user’s utility preference • We can derive: λ = αand μ = -β • For utility model, we have: is the flip of sij

19. An important result For a specific privacy setting over theta_i Privacy rating + utility rating ≈ a constant Privacy-utility are linearly related

20. The weighted/personalized utility model • Users often have clear intention for using SN but have less knowledge on privacy • Users want to put higher utility weight on (a) certain group(s) of profile items than others • Users can assign specific weights to profile items to express his/her preference The utility IRT model can be revised with a weighted model (skip the details here)

21. Illustration of Tradeoff between privacy and utility

22. The Experiments

23. The Real Data from Facebook • Data crawled from Facebook with two accounts • Account normal: a normal Facebook account, which has a certain number of friends • Account fake: a fake account with no friends • Data crawling steps • For the friends and “friends of friends” (FOF) of account normal, crawl the profile item visibility of each user • For the same group of users, crawl the visibility of the fake account’s FoFs’ profile items again • We have the following inference rules

24. Deriving privacy settings of users • Based on the data crawled with the FoF of the two accounts, we derive the (gross) privacy setting of a user based on the following rules E: everyone, FoF: friends of Friends, O:the account owner, F: Friends only

25. Experimental Results

28. Validated with 5-fold cross-validation With p-value <0.05

29. Privacy rating real setting

30. Results of learning weighted utility model

31. Tradeoff between privacy and utility (unweighted) Few people have very high level of privacy concern Privacy rating More people tend to have lower privacy ratings, or implicitly higher utility ratings Utility rating

32. Tradeoff between privacy and weighted utility

33. Conclusion • A framework to address the tradeoff between privacy and utility • Latent trait model (IRT) is used for modeling privacy and utility • We develop a personalized utility model and a tradeoff method for users to find optimal configuration based on utility preferences • The models are validated with a large dataset crawled from Facebook