The Data-Correcting Algorithm for the Minimization of Supermodular Functions
Management Science
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
Convex Optimization
A study of preferences for sharing and privacy
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Security and Privacy
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
Privacy-enhancing personalized web search
Proceedings of the 16th international conference on World Wide Web
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Maximizing Non-Monotone Submodular Functions
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Models of searching and browsing: languages, studies, and applications
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Beyond k-anonymity: a decision theoretic framework for assessing privacy risk
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Learning to Disambiguate Search Queries from Short Sessions
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Privacy enhancements for mobile and social uses of consumer electronics
IEEE Communications Magazine
A utility-theoretic approach to privacy in online services
Journal of Artificial Intelligence Research
Journal of Theoretical and Applied Electronic Commerce Research
Predictive client-side profiles for personalized advertising
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-intimacy tradeoff in self-disclosure
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Privacy and self-disclosure in multiagent systems
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Self-disclosure decision making based on intimacy and privacy
Information Sciences: an International Journal
Privacy-aware personalization for mobile advertising
Proceedings of the 2012 ACM conference on Computer and communications security
Magentix2: A privacy-enhancing Agent Platform
Engineering Applications of Artificial Intelligence
SPARSI: partitioning sensitive data amongst multiple adversaries
Proceedings of the VLDB Endowment
From devices to people: attribution of search activity in multi-user settings
Proceedings of the 23rd international conference on World wide web
Strategies for avoiding preference profiling in agent-based e-commerce environments
Applied Intelligence
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Online services such as web search, news portals, and e-commerce applications face the challenge of providing high-quality experiences to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by personalizing services based on special knowledge about users. For example, a user's location, demographics, and search and browsing history may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information in return for enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can identify a near-optimal solution to the utility-privacy tradeoff. We evaluate the methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users' preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples' willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using only a small amount of information about users.