Abstract
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 privacy-preserving 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.
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Acknowledgments and Disclaimer
Partial support to this work has been received from the European Commission (projects H2020-644024 “CLARUS” and H2020-700540 “CANVAS”), from the Government of Catalonia (ICREA Acadèmia Prize to J. Domingo-Ferrer and grant 2014 SGR 537), and from the Spanish Government (project TIN2014-57364-C2-1-R “SmartGlacis” and TIN 2015-70054-REDC). The authors are with the UNESCO Chair in Data Privacy,but the views in this paper are the authors’ own and are not necessarily shared by UNESCO.
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Ricci, S., Domingo-Ferrer, J., Sánchez, D. (2016). Privacy-Preserving Cloud-Based Statistical Analyses on Sensitive Categorical Data. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Yañez, C. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2016. Lecture Notes in Computer Science(), vol 9880. Springer, Cham. https://doi.org/10.1007/978-3-319-45656-0_19
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