An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Privacy-Preserving Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A new scheme on privacy preserving association rule mining
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
SVD-based collaborative filtering with privacy
Proceedings of the 2005 ACM symposium on Applied computing
A new scheme on privacy-preserving data classification
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Protection and retrieval of encrypted multimedia content: when cryptography meets signal processing
EURASIP Journal on Information Security
Formal apparatus for measurement of lightweight protocols
Computer Standards & Interfaces
Reliable medical recommendation systems with patient privacy
Proceedings of the 1st ACM International Health Informatics Symposium
Pistis: A Privacy-Preserving Content Recommender System for Online Social Communities
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
YANA: an efficient privacy-preserving recommender system for online social communities
Proceedings of the 20th ACM international conference on Information and knowledge management
The decreasing marginal value of evaluation network size
ACM SIGCAS Computers and Society
The impact of data obfuscation on the accuracy of collaborative filtering
Expert Systems with Applications: An International Journal
Privacy-preserving SOM-based recommendations on horizontally distributed data
Knowledge-Based Systems
Reliable medical recommendation systems with patient privacy
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
| Hi-index | 0.00 |
An important security concern with traditional recommendation systems is that users disclose information that may compromise their individual privacy when providing ratings. A randomization approach has been proposed to disguise user ratings while still producing accurate recommendations. However, recent research has suggested that a significant amount of original private information can be derived from perturbed data in a randomization scheme. We suggest that a main limitation of the existing randomization approach is that perturbation is item-invariant--each item has a same perturbation variance. Based on this observation, we introduce a two-way communication privacypreserving scheme in which users perturb their ratings for each item based on the server's guidance instead of using an item-invariant perturbation. Compared to the existing randomization approach, our new scheme can help users disclose much less private information at the same recommendation accuracy level.