Outsourcing Computation on Non-Encrypted SensitiveData to Untrusted Clouds

Identifier:  TDX:2810

Handle: https://hdl.handle.net/20.500.11797/TDX2810

Authors:  Ricci, Sara

Abstract:
Storing and processing big data in local premises is increasingly inconvenient, but resorting to cloud storage and processing raises security and privacy issues. We tackle here the problem of outsourcing to untrusted clouds in a practical and privacy-preserving manner two basic operations on non-encrypted sensitive data: scalar products and matrix products. These operations are useful to perform data analyses such as correlations between attributes or contingency tables, among others. Our protocols allow using the cloud not only to store sensitive non-encrypted data, but also to process them. In addition to analyzing the security of the proposed protocols, we also evaluate their performance against a baseline consisting of downloading plus local computation. The experiments on categorical data that we report on the Amazon cloud service show that, with our protocols, the data controller can save more than 99.999% runtime for the most demanding computations. We also present here a methodology to compare statistical disclosure control (SDC) methods for microdata in terms of how they perform regarding the risk-utility trade-off. Previous comparative studies usually start by selecting some parameter values for a set of SDC methods and evaluate the disclosure risk and the information loss yielded by the methods for those parameterizations. In contrast, here we start by setting a certain risk level and then we find which parameter values are needed to attain that risk under different SDC methods. Finally, we evaluate the utility provided by each method, in order to rank methods according to their utility preservation.