We consider the problem of privacy-preserving cloud-based statistical computation on sensitive categorical data. Specifically, we focus on protocols to obtain the contingency matrix and the sample covariance matrix of the categorical data set. A multi-cloud is used not only to store the sensitive data but also to perform computations on them. However, the multi-cloud is semi-honest, that is, it follows the protocols but is not authorized to learn the sensitive data. Hence, the data must be stored and computed on by the multi-cloud in a privacypreserving format, which we choose to be vertical splitting among the various clouds. We give a comparison of our proposals, based on the secure scalar product, against a benchmark protocol consisting of downloading plus local computation.
CITATION STYLE
Ricci, S., Domingo-Ferrer, J., & Sánchez, D. (2016). Privacy-preserving cloud-based statistical analyses on sensitive categorical data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9880 LNAI, pp. 227–238). Springer Verlag. https://doi.org/10.1007/978-3-319-45656-0_19
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