Abstract
Data privacy and query performance are two closely linked and inconsistent challenges for outsourced databases. Using mixed encryption methods on data attributes can partially reach a trade-off between the two challenges. However, encryption cannot always hide the correlations between attribute values. When the data tuples are accessed selectively, inferences based on comparing encrypted values could be launched, and some sensitive values may be disclosed. In this paper, we explore the intra-attribute based and inter-attribute based inferences in mixed encrypted databases. We develop a method to construct private indexes on encrypted values to defend against those inferences while supporting efficient selective access to encrypted data. We have conducted some experiments to validate our proposed method.
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Tang, Y., Zhang, X., Zhang, J. (2016). Private Indexes for Mixed Encrypted Databases. In: Hameurlain, A., Küng, J., Wagner, R., Dang, T., Thoai, N. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIII. Lecture Notes in Computer Science(), vol 9480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49175-1_5
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DOI: https://doi.org/10.1007/978-3-662-49175-1_5
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