Protecting Encrypted Data against Inference Attacks in Outsourced Databases
Xiaolei Zhang, Yi Tang · Applied Mechanics and Materials · 2014
Ensuring data privacy and improving query performance are two closely linked challenges for outsourced databases. Using mixed encryption methods to data attributes can reach an explicit trade-off between these two challenges. However, encryption cannot always conceal relations between attributes values. When the data tuples are accessed selectively, inferences based on comparing encrypted values could be launched and sensitive values may be disclosed. In this paper, we explore the attribute based inferences in mixed encrypted databases. We develop a method to construct private indexes on encrypted values to defend against inference while supporting efficient selective access to encrypted data. We have conducted some experiments to validate our proposed method.