Automatic text processing
The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Adaptively secure multi-party computation
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Learning object identification rules for information integration
Information Systems - Data extraction, cleaning and reconciliation
Approximate String Joins in a Database (Almost) for Free
Proceedings of the 27th 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
Text joins in an RDBMS for web data integration
WWW '03 Proceedings of the 12th international conference on World Wide Web
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Topk Queries across Multiple Private Databases
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Privacy preserving schema and data matching
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Geocode Matching and Privacy Preservation
Privacy, Security, and Trust in KDD
Formal anonymity models for efficient privacy-preserving joins
Data & Knowledge Engineering
Secure construction of k-unlinkable patient records from distributed providers
Artificial Intelligence in Medicine
Private record matching using differential privacy
Proceedings of the 13th International Conference on Extending Database Technology
A privacy preserving efficient protocol for semantic similarity join using long string attributes
Proceedings of the 4th International Workshop on Privacy and Anonymity in the Information Society
Protecting Privacy Against Record Linkage Disclosure: A Bounded Swapping Approach for Numeric Data
Information Systems Research
Efficient Privacy Preserving Protocols for Similarity Join
Transactions on Data Privacy
Reference table based k-anonymous private blocking
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Efficient and Practical Approach for Private Record Linkage
Journal of Data and Information Quality (JDIQ)
Frequent grams based embedding for privacy preserving record linkage
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient privacy-aware record integration
Proceedings of the 16th International Conference on Extending Database Technology
LinkIT: privacy preserving record linkage and integration via transformations
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A taxonomy of privacy-preserving record linkage techniques
Information Systems
An efficient two-party protocol for approximate matching in private record linkage
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Efficient two-party private blocking based on sorted nearest neighborhood clustering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Mining frequent patterns with differential privacy
Proceedings of the VLDB Endowment
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In this paper, the problem of quickly matching records (i.e., record linkage problem) from two autonomous sources without revealing privacy to the other parties is considered. In particular, our focus is to devise secure blocking scheme to improve the performance of record linkage significantly while being secure. Although there have been works on private record linkage, none has considered adopting the blocking framework. Therefore, our proposed blocking-aware private record linkage can perform large-scale record linkage without revealing privacy. Preliminary experimental results showing the potential of the proposal are reported.