A maximum entropy approach to natural language processing
Computational Linguistics
Policy management using access control spaces
ACM Transactions on Information and System Security (TISSEC)
The Journal of Machine Learning Research
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Detecting network-wide and router-specific misconfigurations through data mining
IEEE/ACM Transactions on Networking (TON)
When social networks cross boundaries: a case study of workplace use of facebook and linkedin
Proceedings of the ACM 2009 international conference on Supporting group work
Proceedings of the fourth international conference on Communities and technologies
Evaluating classifiers by means of test data with noisy labels
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Moving beyond untagging: photo privacy in a tagged world
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Privacy wizards for social networking sites
Proceedings of the 19th international conference on World wide web
Unsupervised modeling of Twitter conversations
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Social links from latent topics in Microblogs
WSA '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media
Baaz: a system for detecting access control misconfigurations
USENIX Security'10 Proceedings of the 19th USENIX conference on Security
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Detecting and resolving policy misconfigurations in access-control systems
ACM Transactions on Information and System Security (TISSEC)
A3P: adaptive policy prediction for shared images over popular content sharing sites
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Analyzing facebook privacy settings: user expectations vs. reality
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Detecting and resolving privacy conflicts for collaborative data sharing in online social networks
Proceedings of the 27th Annual Computer Security Applications Conference
"I regretted the minute I pressed share": a qualitative study of regrets on Facebook
Proceedings of the Seventh Symposium on Usable Privacy and Security
Tag, you can see it!: using tags for access control in photo sharing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Measuring user confidence in smartphone security and privacy
Proceedings of the Eighth Symposium on Usable Privacy and Security
Goldilocks and the two mobile devices: going beyond all-or-nothing access to a device's applications
Proceedings of the Eighth Symposium on Usable Privacy and Security
Facebook and privacy: it's complicated
Proceedings of the Eighth Symposium on Usable Privacy and Security
Are privacy concerns a turn-off?: engagement and privacy in social networks
Proceedings of the Eighth Symposium on Usable Privacy and Security
+Your circles: sharing behavior on Google+
Proceedings of the Eighth Symposium on Usable Privacy and Security
The PViz comprehension tool for social network privacy settings
Proceedings of the Eighth Symposium on Usable Privacy and Security
C4PS - helping facebookers manage their privacy settings
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Mobile advertising: evaluating the effects of animation, user and content relevance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Privacy nudges for social media: an exploratory Facebook study
Proceedings of the 22nd international conference on World Wide Web companion
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As the amount of content users publish on social networking sites rises, so do the danger and costs of inadvertently sharing content with an unintended audience. Studies repeatedly show that users frequently misconfigure their policies or misunderstand the privacy features offered by social networks. A way to mitigate these problems is to develop automated tools to assist users in correctly setting their policy. This paper explores the viability of one such approach: we examine the extent to which machine learning can be used to deduce users' sharing preferences for content posted on Facebook. To generate data on which to evaluate our approach, we conduct an online survey of Facebook users, gathering their Facebook posts and associated policies, as well as their intended privacy policy for a subset of the posts. We use this data to test the efficacy of several algorithms at predicting policies, and the effects on prediction accuracy of varying the features on which they base their predictions. We find that Facebook's default behavior of assigning to a new post the privacy settings of the preceding one correctly assigns policies for only 67% of posts. The best of the prediction algorithms we tested outperforms this baseline for 80% of participants, with an average accuracy of 81%; this equates to a 45% reduction in the number of posts with misconfigured policies. Further, for those participants (66%) whose implemented policy usually matched their intended policy, our approach predicts the correct privacy settings for 94% of posts.