Abstract
Encryption is a well-studied technique for protecting the privacy of sensitive data. However, encrypting relational databases affects the performance during query processing. Multivalued-Partial Order Preserving Encryption Scheme (MV-POPES) allows privacy preserving queries over encrypted databases with reasonable overhead and an improved security level. It divides the plaintext domain into many partitions and randomizes them in the encrypted domain. Then, one integer value is encrypted to different multiple values to prevent statistical attacks. At the same time, MV-POPES preserves the order of the integer values within the partitions to allow comparison operations to be directly applied on encrypted data. However, MV-POPES supports range queries at a high overhead. In this paper, we present some optimization techniques to reduce the overhead for range queries in MV-POPES by simplifying the translated condition and controlling the randomness of the encrypted partitions. The basic idea of our approaches is to classify the partitions into many supersets of partitions, then restrict the randomization within each superset. The supersets of partitions are created either based on predefined queries or using binary recursive partition. Experiments show high improvement percentage in performance using the proposed optimization approaches. Also, we study the affect of those optimization techniques on the privacy level of the encrypted data.
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Kadhem, H., Amagasa, T., Kitagawa, H. (2013). Optimization Techniques for Range Queries in the Multivalued-partial Order Preserving Encryption Scheme. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2010. Communications in Computer and Information Science, vol 272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29764-9_23
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DOI: https://doi.org/10.1007/978-3-642-29764-9_23
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