Multidimensional access methods
ACM Computing Surveys (CSUR)
A new public key cryptosystem based on higher residues
CCS '98 Proceedings of the 5th ACM conference on Computer and communications security
Investigative Data Mining for Security and Criminal Detection
Investigative Data Mining for Security and Criminal Detection
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
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
Privacy-Preserving Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Private Representative-Based Clustering for Vertically Partitioned Data
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
The privacy of k-NN retrieval for horizontal partitioned data: new methods and applications
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
A new efficient privacy-preserving scalar product protocol
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
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Clustering algorithms are attractive for the core task of class identification in large databases. In recent years privacy issues also became important for data mining. In this paper, we construct a privacy preserving version of the popular clustering algorithm DBSCAN. This algorithm is density-based. Such notion of clustering allows us to discover clusters of arbitrary shape. DBSCAN requires only two input parameters, but it offers some support in determining appropriate values. Originally, DBSCAN uses R-trees to support efficient associative queries. Thus, one solution for privacy preserving DBSCAN requires to have privacy preserving R-trees. We achieve this here.