Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Improving the Efficiency of Interactive Sequential Pattern Mining by Incremental Pattern Discovery
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 3 - Volume 3
Discussion paper: privacy-preserving distributed queries for a clinical case research network
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
A data mining approach for database intrusion detection
Proceedings of the 2004 ACM symposium on Applied computing
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A cubic-wise balance approach for privacy preservation in data cubes
Information Sciences: an International Journal
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
Information Sciences: an International Journal
Discovering Patterns in Flows: A Privacy Preserving Approach with the ACSM Prototype
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Hiding collaborative recommendation association rules on horizontally partitioned data
Intelligent Data Analysis
Privacy-preserving data mining: A feature set partitioning approach
Information Sciences: an International Journal
Learning latent variable models from distributed and abstracted data
Information Sciences: an International Journal
Privacy-preserving disjunctive normal form operations on distributed sets
Information Sciences: an International Journal
Application traffic classification at the early stage by characterizing application rounds
Information Sciences: an International Journal
Two machine-learning techniques for mining solutions of the ReleasePlannerTM decision support system
Information Sciences: an International Journal
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As the total amount of traffic data in networks has been growing at an alarming rate, there is currently a substantial body of research that attempts to mine traffic data with the purpose of obtaining useful information. For instance, there are some investigations into the detection of Internet worms and intrusions by discovering abnormal traffic patterns. However, since network traffic data contain information about the Internet usage patterns of users, network users' privacy may be compromised during the mining process. In this paper, we propose an efficient and practical method that preserves privacy during sequential pattern mining on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model, which operates as a single mining server and the retention replacement technique, which changes the answer to a query probabilistically. In addition, our method accelerates the overall mining process by maintaining the meta tables in each site so as to determine quickly whether candidate patterns have ever occurred in the site or not. Extensive experiments with real-world network traffic data revealed the correctness and the efficiency of the proposed method.