ConNexus to awarenex: extending awareness to mobile users
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Location disclosure to social relations: why, when, & what people want to share
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Privacy in Location-Aware Computing Environments
IEEE Pervasive Computing
Proceedings of the 6th ACM conference on Embedded network sensor systems
Understanding and capturing people's privacy policies in a mobile social networking application
Personal and Ubiquitous Computing
From spaces to places: emerging contexts in mobile privacy
Proceedings of the 11th international conference on Ubiquitous computing
Inferring privacy policies for social networking services
Proceedings of the 2nd ACM workshop on Security and artificial intelligence
Uncertain inference control in privacy protection
International Journal of Information Security
Field deployment of IMBuddy: a study of privacy control and feedback mechanisms for contextual IM
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Privacy wizards for social networking sites
Proceedings of the 19th international conference on World wide web
Empirical models of privacy in location sharing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Locaccino: a privacy-centric location sharing application
Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Adjunct
Towards security policy decisions based on context profiling
Proceedings of the 3rd ACM workshop on Artificial intelligence and security
LittleRock: Enabling Energy-Efficient Continuous Sensing on Mobile Phones
IEEE Pervasive Computing
Proceedings of the 13th international conference on Ubiquitous computing
Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs
Personal and Ubiquitous Computing
Social disclosure of place: from location technology to communication practices
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
The mismeasurement of privacy: using contextual integrity to reconsider privacy in HCI
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The implications of offering more disclosure choices for social location sharing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Progressive authentication: deciding when to authenticate on mobile phones
Security'12 Proceedings of the 21st USENIX conference on Security symposium
My privacy policy: exploring end-user specification of free-form location access rules
FC'12 Proceedings of the 16th international conference on Financial Cryptography and Data Security
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Personal and contextual information are increasingly shared via mobile social networks. Users' locations, activities and their co-presence can be shared easily with online "friends", as their smartphones already access such information from embedded sensors and storage. Yet, people usually exhibit selective sharing behavior depending on contextual attributes, thus showing that privacy, utility, and usability are paramount to the success of such online services. In this paper, we present SPISM, a novel information-sharing system that decides (semi-)automatically whether to share information with others, whenever they request it, and at what granularity. Based on active machine learning and context, SPISM adapts to each user's behavior and it predicts the level of detail for each sharing decision, without revealing any personal information to a third-party. Based on a personalized survey about information sharing involving 70 participants, our results provide insight into the most influential features behind a sharing decision. Moreover, we investigate the reasons for the users' decisions and their confidence in them. We show that SPISM outperforms other kinds of global and individual policies, by achieving up to 90% of correct decisions.