To preserve private information while providing thorough analysis is one of the significant issues in OLAP systems. One of the challenges in it is to prevent inferring the sensitiv...
Ming Hua, Shouzhi Zhang, Wei Wang 0009, Haofeng Zh...
We present techniques for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients. Data from different clients is perturbed (rand...
Rakesh Agrawal, Ramakrishnan Srikant, Dilys Thomas
In this paper we are interested in storing and perform OLAP queries about various aggregate trajectory properties. We consider a data stream environment where a set of mobile objec...
—We demonstrate DPCube, a component in our Health Information DE-identification (HIDE) framework, for releasing differentially private data cubes (or multi-dimensional histogram...