Full Citation
Title: Multi-dimensional indexes for point and range queries on outsourced encrypted data
Citation Type: Conference Paper
Publication Year: 2021
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ISSN:
DOI: 10.1109/GLOBECOM46510.2021.9685186
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Abstract: We present an approach for indexing encrypted data stored at external providers to enable provider-side evaluation of queries. Our approach supports the evaluation of point and range conditions on multiple attributes. Protection against inferences from indexes is guaranteed by clustering tuples in boxes that are then mapped to the same index values, so to ensure collisions for individual attributes as well as their combinations. Our spatialbased algorithm partitions tuples to produce such a clustering in a way to ensure efficient query execution. Query translation and processing require the client to store a compact map. The experiments, evaluating query performance and client-storage requirements, confirm the efficiency enjoyed by our solution.
Url: https://doi.org/10.1109/GLOBECOM46510.2021.9685186
Url: https://cs.unibg.it/seclab-papers/2021/GLOBECOM/multi-dimensional-indexes.pdf
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Authors: di Vimercati, Sabrina De Capitani; Facchinetti, Dario; Faresti, Sara; Oldani, Gianluca; Paraboschi, Stefano; Rossi, Matthew; Samarati, Pierangela
Conference Name: 2021 IEEE Global Communications Conference (GLOBECOM)
Publisher Location:
Data Collections: IPUMS USA
Topics: Methodology and Data Collection
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