Abstract
The paper introduces the concept of \(\varPhi \)-data, data that is a proxy for some underlying data that offers advantages of data privacy and security while at the same time allowing particular data mining operations without requiring data owner participation once the proxy has been generated. The nature of the proxy representation is dependent on the nature of the desired data mining to be undertaken. Secure collaborative clustering is considered where the \(\varPhi \)-data is in the form of a Super Secure Chain Distance Matrices (SSCDM) encrypted using a proposed Multi-User Order Preserving Encryption (MUOPE) scheme. SSCDMs can be produced with respect to horizontal and vertical data partitioning. The DBSCAN clustering algorithm is adopted for illustrative and evaluation purposes. The results indicate that the proposed solution is efficient and produces comparable clustering configurations to those produced using an unencrypted, “standard”, algorithm; while maintaining data privacy and security.
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Almutairi, N., Coenen, F., Dures, K. (2018). Secure Third Party Data Clustering Using \(\varPhi \) Data: Multi-User Order Preserving Encryption and Super Secure Chain Distance Matrices (Best Technical Paper). In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_1
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