Opinion-Based Filtering through Trust
CIA '02 Proceedings of the 6th International Workshop on Cooperative Information Agents VI
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
On The Linear Combination Of Laplace Random Variables
Probability in the Engineering and Informational Sciences
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Ostra: leveraging trust to thwart unwanted communication
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
Do social networks improve e-commerce?: a study on social marketplaces
Proceedings of the first workshop on Online social networks
Alambic: a privacy-preserving recommender system for electronic commerce
International Journal of Information Security
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Accurate Estimation of the Degree Distribution of Private Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Data publishing against realistic adversaries
Proceedings of the VLDB Endowment
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Feeding frenzy: selectively materializing users' event feeds
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Discovering frequent patterns in sensitive data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
"You Might Also Like: " Privacy Risks of Collaborative Filtering
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Privacy-aware data management in information networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Hiding data and structure in workflow provenance
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Differential privacy in data publication and analysis
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
PointBurst: towards a trust-relationship framework for improved social recommendations
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Privacy consensus in anonymization systems via game theory
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
Challenges in enabling social application at scale: cloudDB'12 invited-keynote talk abstract
Proceedings of the fourth international workshop on Cloud data management
Social filtering using social relationship for movie recommendation
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Structural and Message Based Private Friend Recommendation
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
A Guide to Differential Privacy Theory in Social Network Analysis
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Knowledge-Based Systems
Mining frequent graph patterns with differential privacy
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Know your personalization: learning topic level personalization in online services
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
Pufferfish: A framework for mathematical privacy definitions
ACM Transactions on Database Systems (TODS)
Efficient Time-Stamped Event Sequence Anonymization
ACM Transactions on the Web (TWEB)
Towards comprehensive social sharing of recommendations: augmenting push with pull
Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks
Dynamic enforcement of knowledge-based security policies using probabilistic abstract interpretation
Journal of Computer Security
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With the recent surge of social networks such as Facebook, new forms of recommendations have become possible -- recommendations that rely on one's social connections in order to make personalized recommendations of ads, content, products, and people. Since recommendations may use sensitive information, it is speculated that these recommendations are associated with privacy risks. The main contribution of this work is in formalizing trade-offs between accuracy and privacy of personalized social recommendations. We study whether "social recommendations", or recommendations that are solely based on a user's social network, can be made without disclosing sensitive links in the social graph. More precisely, we quantify the loss in utility when existing recommendation algorithms are modified to satisfy a strong notion of privacy, called differential privacy. We prove lower bounds on the minimum loss in utility for any recommendation algorithm that is differentially private. We then adapt two privacy preserving algorithms from the differential privacy literature to the problem of social recommendations, and analyze their performance in comparison to our lower bounds, both analytically and experimentally. We show that good private social recommendations are feasible only for a small subset of the users in the social network or for a lenient setting of privacy parameters.