Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Blocking-aware private record linkage
Proceedings of the 2nd international workshop on Information quality in information systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
Privacy preserving schema and data matching
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
A Hybrid Approach to Private Record Linkage
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient Private Record Linkage
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Private record matching using differential privacy
Proceedings of the 13th International Conference on Extending Database Technology
Discovering frequent patterns in sensitive data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
PrivBasis: frequent itemset mining with differential privacy
Proceedings of the VLDB Endowment
Differentially private sequential data publication via variable-length n-grams
Proceedings of the 2012 ACM conference on Computer and communications security
Frequent grams based embedding for privacy preserving record linkage
Proceedings of the 21st ACM international conference on Information and knowledge management
On differentially private frequent itemset mining
Proceedings of the VLDB Endowment
Efficient privacy-aware record integration
Proceedings of the 16th International Conference on Extending Database Technology
Hi-index | 0.00 |
The mining of frequent patterns is a fundamental component in many data mining tasks. A considerable amount of research on this problem has led to a wide series of efficient and scalable algorithms for mining frequent patterns. However, releasing these patterns is posing concerns on the privacy of the users participating in the data. Indeed the information from the patterns can be linked with a large amount of data available from other sources creating opportunities for adversaries to break the individual privacy of the users and disclose sensitive information. In this proposal, we study the mining of frequent patterns in a privacy preserving setting. We first investigate the difference between sequential and itemset patterns, and second we extend the definition of patterns by considering the absence and presence of noise in the data. This leads us in distinguishing the patterns between exact and noisy. For exact patterns, we describe two novel mining techniques that we previously developed. The first approach has been applied in a privacy preserving record linkage setting, where our solution is used to mine frequent patterns which are employed in a secure transformation procedure to link records that are similar. The second approach improves the mining utility results using a two-phase strategy which allows to effectively mine frequent substrings as well as prefixes patterns. For noisy patterns, first we formally define the patterns according to the type of noise and second we provide a set of potential applications that require the mining of these patterns. We conclude the paper by stating the challenges in this new setting and possible future research directions.