Implementing fault-tolerant services using the state machine approach: a tutorial
ACM Computing Surveys (CSUR)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Distributed caching with memcached
Linux Journal
LSH forest: self-tuning indexes for similarity search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Tor: the second-generation onion router
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
An Architecture for Privacy Preserving Collaborative Filtering on Web Portals
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Reconstructing Data Perturbed by Random Projections When the Mixing Matrix Is Known
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Training and testing of recommender systems on data missing not at random
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Privad: practical privacy in online advertising
Proceedings of the 8th USENIX conference on Networked systems design and implementation
RePriv: Re-imagining Content Personalization and In-browser Privacy
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
The GOSSPLE anonymous social network
Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware
Achieving private recommendations using randomized response techniques
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Provable de-anonymization of large datasets with sparse dimensions
POST'12 Proceedings of the First international conference on Principles of Security and Trust
Distributed private heavy hitters
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor
International Journal of Data Warehousing and Mining
A Practical System for Privacy-Preserving Collaborative Filtering
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
On the Use of LSH for Privacy Preserving Personalization
TRUSTCOM '13 Proceedings of the 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
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We present the design, implementation, and evaluation of a decentralized framework for enabling privacy in Web-scale recommendation services. Our framework, which comprises of a decentralized middleware that is hosted and run by federated entities, is designed to support collaborative-filtering and content-based recommendations. We design a novel distributed protocol that clusters users into interest groups comprised of like-minded members and ensures a desired minimum size (k-anonymity parameter), while keeping user profiles on client-side only. In order to aggregate users' consumption for the purpose of generating recommendations, we design a novel decentralized aggregation mechanism that protects against auxiliary information attacks that have crippled conventional k-anonymity based systems. Our prototype system ensures that the desired k-anonymity level is met, and can prevent auxiliary information attacks using a middleware of modest size, and is empirically shown to be resistant to moderate degree of collusion. While preserving privacy, our system enables effective clustering of like-minded users, and offers good quality of recommendations. Also, the prototype's decentralized design and lightweight protocols enable almost linear-scaling with increased size of the middleware.