Personalized social recommendations: accurate or private
Proceedings of the VLDB Endowment
Private and Continual Release of Statistics
ACM Transactions on Information and System Security (TISSEC)
The decreasing marginal value of evaluation network size
ACM SIGCAS Computers and Society
Use fewer instances of the letter "i": toward writing style anonymization
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
Testing the lipschitz property over product distributions with applications to data privacy
TCC'13 Proceedings of the 10th theory of cryptography conference on Theory of Cryptography
πBox: a platform for privacy-preserving apps
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Proceedings of the 22nd international conference on World Wide Web
Differential privacy for neighborhood-based collaborative filtering
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
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Many commercial websites use recommender systems to help customers locate products and content. Modern recommenders are based on collaborative filtering: they use patterns learned from users' behavior to make recommendations, usually in the form of related-items lists. The scale and complexity of these systems, along with the fact that their outputs reveal only relationships between items (as opposed to information about users), may suggest that they pose no meaningful privacy risk. In this paper, we develop algorithms which take a moderate amount of auxiliary information about a customer and infer this customer's transactions from temporal changes in the public outputs of a recommender system. Our inference attacks are passive and can be carried out by any Internet user. We evaluate their feasibility using public data from popular websites Hunch, Last. fm, Library Thing, and Amazon.