Communications of the ACM
PRAW—A PRivAcy model for the Web: Research Articles
Journal of the American Society for Information Science and Technology
A PPM Prediction Model Based on Stochastic Gradient Descent for Web Prefetching
AINA '08 Proceedings of the 22nd International Conference on Advanced Information Networking and Applications
Predicting future locations using prediction-by-partial-match
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Privacy diffusion on the web: a longitudinal perspective
Proceedings of the 18th international conference on World wide web
Noise Injection for Search Privacy Protection
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
An integrated model for next page access prediction
International Journal of Knowledge and Web Intelligence
Web latency reduction with prefetching
Web latency reduction with prefetching
Optimized query forgery for private information retrieval
IEEE Transactions on Information Theory
I know what you will do next summer
ACM SIGCOMM Computer Communication Review
On the privacy of web search based on query obfuscation: a case study of TrackMeNot
PETS'10 Proceedings of the 10th international conference on Privacy enhancing technologies
Mobile web profiling: a study of off-portal surfing habits of mobile users
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Investigation of Context Prediction Accuracy for Different Context Abstraction Levels
IEEE Transactions on Mobile Computing
Obfuscating the Topical Intention in Enterprise Text Search
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
OB-PWS: Obfuscation-Based Private Web Search
SP '12 Proceedings of the 2012 IEEE Symposium on Security and Privacy
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Information about consumer's web navigation trails are increasingly being collected, analyzed, and used to target them with advertisements. This information can be quite personal, indicating individuals' likes and dislikes, as well as current and long term needs. While the ability to effectively target advertisements helps keep many sites and services on the web available freely, this practice has also raised privacy concerns. These concerns concern multiple factors: lack of transparency by the data aggregators and lack of control by the consumers. One viable approach that individuals can take to regain control is obfuscation, whereby real user requests are masked via the injection of noisy requests. In this paper, we describe a theoretical model and design for a web browser extension that relies on a trusted third party to generate fake HTTP requests (dummies). The dummy requests are generated as k different users' profiles surfing in parallel with the actual user. The value of k can be adjusted by the user to achieve the level of obfuscation they are comfortable with.