A secure multiparty computation privacy preserving OLAP framework over distributed XML data

Abstract

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

Towards a theory for privacy preserving distributed OLAP

“…In order to solve research challenges deriving from the issue of computing and managing privacy preserving data cubes paradigm in distributed environments, in [9] we introduced an innovative privacy preserving distributed OLAP framework that relies on the novel concept of the so-called secure distributed OLAP aggregation task. Basically, this task purses the idea of performing OLAP across multiple distributed SUM-based twodimensional OLAP views extracted from data cubes under the SMC requirements [9]. To this end, the Secure Distributed OLAP aggregation protocol (SDO) has been introduced and experimentally assessed in [9].…”

“…Basically, this task purses the idea of performing OLAP across multiple distributed SUM-based twodimensional OLAP views extracted from data cubes under the SMC requirements [9]. To this end, the Secure Distributed OLAP aggregation protocol (SDO) has been introduced and experimentally assessed in [9]. Briefly, the SDO protocol works as follows [9].…”

“…To this end, the Secure Distributed OLAP aggregation protocol (SDO) has been introduced and experimentally assessed in [9]. Briefly, the SDO protocol works as follows [9]. Having fixed a certain node ordering (e.g., DNSbased), the first node N 0 in the distributed environment computes a privacy preserving version of its proper OLAP view, denoted by V 0 , and sends to the second node N 1 in the distributed environment that, in turn, (i) combines with its…”

“…Particularly, in [9] we specifically focus the attention on the class of SUM-based distributed OLAP aggregation tasks, being SUM a popular aggregate operator for OLAP applications (e.g., [19]). Despite this, our framework [9] is general enough to deal with more sophisticated distributed OLAP aggregation tasks that embed complex OLAP aggregations (e.g., [18]) rather than conventional ones (e.g., SUM, COUNT, AVG).…”

“…Despite this, our framework [9] is general enough to deal with more sophisticated distributed OLAP aggregation tasks that embed complex OLAP aggregations (e.g., [18]) rather than conventional ones (e.g., SUM, COUNT, AVG). Also, while our framework [9] is general enough to deal with OLAP views computed over any arbitrary kind of data sources, in [9] we considered the specialized applicative case represented by data cubes computed on top of distributed collections of XML documents, which are more and more relevant for BI applications. The core of the framework [9] is represented by the CUR matrix decomposition technique [14], which allows us to computed privacy preserving two-dimensional OLAP views effectively and efficiently, at a provable approximation error [9].…”