1. Adaptive Information-Sharing for Privacy-Aware Mobile Social Network Igor Bilogrevic 1, Kévin Huguenin 1, Berker Agir 1, Murtuza Jadliwala 2 and Jean-Pierre Hubaux 1, EPFL-IC-LCA1 Switzerland 2, Wichita State University USA UbiComp 2013

2. Outline INTRODUCTION RELATED WORK THE SPISM INFORMATION-SHARING PLATFORM STUDY AND DATA COLLECTION ANALYSIS AND EVALUATION MACHINE LEARNING CONCLUSION AND FUTURE WORK

3. INTRODUCTION(1/2) The recurrent finding of contextual information-sharing in UbiComp studies: – Users are not particularly good at effectively articulating their information-sharing policies. – Sharing policies evolve over time Machine learning – Context, user-burden trade-off and privacy

4. INTRODUCTION(2/2) The contribution of this work: – Developing a novel information-sharing system(SPISM) – Conducting a personalized online study involving 70 participants – Evaluating SPISM with respect to the amount of training data

5. RELATED WORK(1/2) Contextual Information Sharing and Privacy – Increasing the flexibility of the decision making process – Evaluation – Optimizing automated information-sharing mechanisms

6. RELATED WORK(2/2) Machine Learning and Information Sharing – Feedback in the learning mechanism – Decision behavior can change over time – Extract social groups or communities

7. THE SPISM INFORMATION-SHARING PLATFORM(1/4) Two subscribers(users, third-party online services or mobile apps) to SPISM: – Requester, who wants to know something about other subscribers by sending information requests – Target, who receives requests for information The information – Contextual data – Time-schedule availability

8. THE SPISM INFORMATION-SHARING PLATFORM(2/4)

9. THE SPISM INFORMATION-SHARING PLATFORM(3/4) Decision making – In order to make the decision, several contextual features are taken into account by the target device(social ties, current location, activity…)

10. THE SPISM INFORMATION-SHARING PLATFORM(4/4)

11. STUDY AND DATA COLLECTION Participants – Between 18 and 80 – With an active Facebook with at least 50 friends – Using a smartphone Online survey – 19 fixed questions – 75 personalized questions

12. ANALYSIS AND EVALUATION(1/3) Results – Using descriptive statistics of the survey questionnaire – Comparing the performance of the SPISM automated decision-making process – Discussing the effects of the increase of user- involvement on the performance of SPISM

13. ANALYSIS AND EVALUATION(2/3)

14. ANALYSIS AND EVALUATION(3/3)

15. MACHINE LEARNING(1/2) use the survey data comprised of 75 scenarios for each of the 70 participants – match the user’s decision Training – Users make n decisions and classifier makes remaining decisions – Representing the burden of the user as she has to manually make the corresponding decisions

16. MACHINE LEARNING(2/2) Training + Learning – active learning can be used to improve the correctness of the sharing decisions while maintaining a significantly lower over-sharing rate

17. CONCLUSION AND FUTURE WORK SPISM significantly outperforms both individual and general user-defined sharing policies Slight bias towards incorrectly sharing the information Showing that the type of the requested information, in addition to the social ties of the requester, is an influential feature in the decision process.