Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
IEEE Transactions on Knowledge and Data Engineering
The VLDB Journal — The International Journal on Very Large Data Bases
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
Privacy preserving decision tree learning over multiple parties
Data & Knowledge Engineering
On static and dynamic methods for condensation-based privacy-preserving data mining
ACM Transactions on Database Systems (TODS)
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
A privacy preserving technique for distance-based classification with worst case privacy guarantees
Data & Knowledge Engineering
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
Privacy Preserving Market Basket Data Analysis
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Searching for Better Randomized Response Schemes for Privacy-Preserving Data Mining
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Protecting business intelligence and customer privacy while outsourcing data mining tasks
Knowledge and Information Systems
Evaluating privacy threats in released database views by symmetric indistinguishability
Journal of Computer Security - Selected papers from the Third and Fourth Secure Data Management (SDM) workshops
Nearest Neighbor Tour Circuit Encryption Algorithm Based Random Isomap Reduction
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Optimal random perturbation at multiple privacy levels
Proceedings of the VLDB Endowment
Privacy Preserving Categorical Data Analysis with Unknown Distortion Parameters
Transactions on Data Privacy
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
On addressing accuracy concerns in privacy preserving association rule mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Privacy-preserving data mining through knowledge model sharing
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
APHID: An architecture for private, high-performance integrated data mining
Future Generation Computer Systems
Discovering frequent patterns in sensitive data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Small domain randomization: same privacy, more utility
Proceedings of the VLDB Endowment
Privacy-aware DaaS services composition
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
SIAM Journal on Computing
Privacy-preserving decision tree mining based on random substitutions
ETRICS'06 Proceedings of the 2006 international conference on Emerging Trends in Information and Communication Security
On robust and effective k-anonymity in large databases
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
An effective approach for hiding sensitive knowledge in data publishing
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
Indistinguishability: the other aspect of privacy
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
Survey: DNA-inspired information concealing: A survey
Computer Science Review
Publishing microdata with a robust privacy guarantee
Proceedings of the VLDB Endowment
A Knowledge Model Sharing Based Approach to Privacy-Preserving Data Mining
Transactions on Data Privacy
Application and analysis of multidimensional negative surveys in participatory sensing applications
Pervasive and Mobile Computing
Dividing secrets to secure data outsourcing
Information Sciences: an International Journal
Distributed and Parallel Databases
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To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of individual data records have been proposed recently. In this paper, we present FRAPP, a generalized matrix-theoretic framework of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, FRAPP is used to demonstrate that (a) the prior techniques differ only in their choices for the perturbation matrix elements, and (b) a symmetric perturbation matrix with minimal condition number can be identified, maximizing the accuracy even under strict privacy guarantees. We also propose a novel perturbation mechanism wherein the matrix elements are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at only a marginal cost in accuracy. The quantitative utility of FRAPP, which applies to random-perturbation-based privacy-preserving mining in general, is evaluated specifically with regard to frequent-itemset mining on a variety of real datasets. Our experimental results indicate that, for a given privacy requirement, substantially lower errors are incurred, with respect to both itemset identity and itemset support, as compared to the prior techniques.