PHOAKS: a system for sharing recommendations
Communications of the ACM
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Privacy in e-commerce: examining user scenarios and privacy preferences
Proceedings of the 1st ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Privacy Risks in Recommender Systems
IEEE Internet Computing
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Proceedings of the 13th international conference on World Wide Web
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
An architecture for privacy-sensitive ubiquitous computing
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Insert movie reference here: a system to bridge conversation and item-oriented web sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
"I know what you did last summer": query logs and user privacy
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Mining recommendations from the web
Proceedings of the 2008 ACM conference on Recommender systems
Vanity fair: privacy in querylog bundles
Proceedings of the 17th ACM conference on Information and knowledge management
Sharescape: an interface for place annotation
Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges
Self-organised virtual communities: bridging the gap between web-based communities and P2P systems
International Journal of Web Based Communities
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
Lurking? cyclopaths?: a quantitative lifecycle analysis of user behavior in a geowiki
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative filtering recommender systems
The adaptive web
Privacy-enhanced web personalization
The adaptive web
Publishing anonymous survey rating data
Data Mining and Knowledge Discovery
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
More than modelling and hiding: towards a comprehensive view of Web mining and privacy
Data Mining and Knowledge Discovery
Provable de-anonymization of large datasets with sparse dimensions
POST'12 Proceedings of the First international conference on Principles of Security and Trust
On the identity anonymization of high-dimensional rating data
Concurrency and Computation: Practice & Experience
On the feasibility of user de-anonymization from shared mobile sensor data
Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones
Theoretical Results on De-Anonymization via Linkage Attacks
Transactions on Data Privacy
A PLA-based privacy-enhancing user modeling framework and its evaluation
User Modeling and User-Adapted Interaction
What's in a name?: an unsupervised approach to link users across communities
Proceedings of the sixth ACM international conference on Web search and data mining
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In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, it may be possible to link these separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This re-identification violates people's intentions to separate aspects of their life and can have negative consequences; it also may allow other privacy violations, such as obtaining a stronger identifier like name and address.This paper examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset. We present three major results. First, we develop algorithms that can re-identify a large proportion of public users in a sparse relation space. Second, we evaluate whether private dataset owners can protect user privacy by hiding data; we show that this requires extensive and undesirable changes to the dataset, making it impractical. Third, we evaluate two methods for users in a public forum to protect their own privacy, suppression and misdirection. Suppression doesn't work here either. However, we show that a simple misdirection strategy works well: mention a few popular items that you haven't rated.