Abstract
Ensuring data protection and enhancing selective query performance over encrypted data are two closely linked challenges for outsourced databases. It needs to develop indexes over encrypted data to support secure and efficient selective queries on server side. However, the plaintext-associated information hidden in those indexes may introduce inference attacks when comparing with different encrypted tuple sets. In this paper, we investigate a kind of inference attacks by linking query results from different database users. The inferences are based on implicit equality relations hidden in query results. To defend against this attack, we develop a generalization-based method to construct secure and private indexes. We design a combined metric to measure the inference resistance of our proposed method. This measure is quantized by the entropy values and attribute value diversities in query results. We have conducted some experiments to validate our proposed method.
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Tang, Y., Liu, F., Huang, L. (2013). Generalization-Based Private Indexes for Outsourced Databases. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37487-6_14
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DOI: https://doi.org/10.1007/978-3-642-37487-6_14
Publisher Name: Springer, Berlin, Heidelberg
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