C4.5: programs for machine learning
C4.5: programs for machine learning
Intelligent integration of information
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
The inference problem: a survey
ACM SIGKDD Explorations Newsletter
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A formal analysis of information disclosure in data exchange
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Anonymity-preserving data collection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymizing sequential releases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A secure distributed framework for achieving k-anonymity
The VLDB Journal — The International Journal on Very Large Data Bases
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Handicapping attacker's confidence: an alternative to k-anonymization
Knowledge and Information Systems
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Anonymity for continuous data publishing
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Privacy-preserving distributed k-anonymity
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
Anonymizing healthcare data: a case study on the blood transfusion service
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Centralized and Distributed Anonymization for High-Dimensional Healthcare Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Anonymity meets game theory: secure data integration with malicious participants
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-aware DaaS services composition
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Secure distributed computation of anonymized views of shared databases
ACM Transactions on Database Systems (TODS)
Anonyfrag: an anonymization-based approach for privacy-preserving BPaaS
Proceedings of the 1st International Workshop on Cloud Intelligence
Distributed data federation without disclosure of user existence
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
Modeling and Respecting Privacy Specification when Composing DaaS Services*
International Journal of Web Services Research
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Mashup is a web technology that combines information from more than one source into a single web application. This technique provides a new platform for different data providers to flexibly integrate their expertise and deliver highly customizable services to their customers. Nonetheless, combining data from different sources could potentially reveal person-specific sensitive information. In this paper, we study and resolve a real-life privacy problem in a data mashup application for the financial industry in Sweden, and propose a privacy-preserving data mashup (PPMashup) algorithm to securely integrate private data from different data providers, whereas the integrated data still retains the essential information for supporting general data exploration or a specific data mining task, such as classification analysis. Experiments on real-life data suggest that our proposed method is effective for simultaneously preserving both privacy and information usefulness, and is scalable for handling large volume of data.