Privacy Preserving Distributed OLAP  is becoming a critical challenge for next-generation  Business Intelligence  (BI) scenarios, due to the "natural suitability" of OLAP in analyzing distributed massive BI repositories in a  multidimensional  and  multigranularity  manner. In particular, in these scenarios  XML-formatted BI repositories  play a dominant role, due to the wellknow amenities of XML in modeling and representing distributed business data. However, while  Privacy Preserving Distributed Data Mining  has been widely investigated, very few efforts have focused on the problem of effectively and efficiently supporting  privacy preserving OLAP over distributed collections of XML documents . In order to fulfill this gap, we propose a novel  Secure Multiparty Computation (SMC)-based privacy preserving OLAP framework for distributed collections of XML documents . The framework has many novel features ranging from nice theoretical properties to an effective and efficient protocol. The efficiency of our approach has been validated by an experimental evaluation over distributed collections of synthetic XML documents.
['Alfredo Cuzzocrea', 'Elisa Bertino']
A secure multiparty computation privacy preserving OLAP framework over distributed XML data