Security checking in relational database management systems augmented with inference engines
Computers and Security
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
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
Secure Databases: Constraints, Inference Channels, and Monitoring Disclosures
IEEE Transactions on Knowledge and Data Engineering
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Datafly: A System for Providing Anonymity in Medical Data
Proceedings of the IFIP TC11 WG11.3 Eleventh International Conference on Database Securty XI: Status and Prospects
The inference problem: a survey
ACM SIGKDD Explorations Newsletter
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
On the value of private information
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Distributed privacy preserving information sharing
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Auditing sum-queries to make a statistical database secure
ACM Transactions on Information and System Security (TISSEC)
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
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Data Mining Applications in the Malicious Model
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Transforming semi-honest protocols to ensure accountability
Data & Knowledge Engineering
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Privacy-preserving data mining in the malicious model
International Journal of Information and Computer Security
Privacy-preserving data mashup
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A Hybrid Approach to Private Record Linkage
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data 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
Distributed Anonymization: Achieving Privacy for Both Data Subjects and Data Providers
Proceedings of the 23rd Annual IFIP WG 11.3 Working Conference on Data and Applications Security XXIII
Private record matching using differential privacy
Proceedings of the 13th International Conference on Extending Database Technology
The hardness and approximation algorithms for l-diversity
Proceedings of the 13th International Conference on Extending Database Technology
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Algorithms for selfish agents mechanism design for distributed computation
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Centralized and Distributed Anonymization for High-Dimensional Healthcare Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Inference aggregation detection in database management systems
SP'88 Proceedings of the 1988 IEEE conference on Security and privacy
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
On honesty in sovereign information sharing
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Privacy-preserving distributed k-anonymity
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Secure distributed framework for achieving ε-differential privacy
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
A taxonomy of privacy-preserving record linkage techniques
Information Systems
Preserving privacy and frequent sharing patterns for social network data publishing
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
An iterative two-party protocol for scalable privacy-preserving record linkage
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Data integration methods enable 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 VLDBJ 2006, Jiang and Clifton (Very Large Data Bases J (VLDBJ) 15(4):316---333, 2006) propose a secure Distributed k-Anonymity (DkA) framework for integrating two private data tables to a k-anonymous table in which each private table is a vertical partition on the same set of records. Their proposed DkA framework is not scalable to large data sets. Moreover, DkA is limited to a two-party scenario and the parties are assumed to be semi-honest. In this paper, we propose two algorithms to securely integrate private data from multiple parties (data providers). Our first algorithm achieves the k-anonymity privacy model in a semi-honest adversary model. Our second algorithm employs a game-theoretic approach to thwart malicious participants and to ensure fair and honest participation of multiple data providers in the data integration process. Moreover, we study and resolve a real-life privacy problem in data sharing for the financial industry in Sweden. Experiments on the real-life data demonstrate that our proposed algorithms can effectively retain the essential information in anonymous data for data analysis and are scalable for anonymizing large data sets.