Link and graph analysis tools are important devices to boost the richness of information retrieval systems. Internet and the existing social networking portals are just a couple of situations where the use of these tools would be beneficial and enriching for the users and the analysts. However, the need for integrating different data sources and, even more important, the need for high performance generic tools, is at odds with the continuously growing size and number of\ud data repositories.\ud In this paper we propose and evaluate DEX, a high performance graph database querying system that allows for the integration of multiple data sources. DEX makes graph\ud querying possible in different flavors, including link analysis, social network analysis, pattern recognition and keyword search. The richness of DEX shows up in the experiments that we carried out on the Internet Movie Database(IMDb). Through a variety of these complex analytical queries, DEX shows to be a generic and efficient tool on large graph databases.Peer ReviewedPostprint (published version

Statistical Disclosure Control (SDC) is an active research area in the recent years. The goal is to transform an original dataset X into a protected one X0, such that X0 does not reveal any relation between confidential and (quasi-)identifier attributes and such that X0 can be\ud used to compute reliable statistical information about X. Many specific protection methods have been proposed and analyzed, with respect to the\ud levels of privacy and utility that they offer. However, when measuring utility, only differences between the statistical values of X and X0 are considered. This would indicate that datasets protected by SDC methods can be used only for statistical purposes.\ud We show in this paper that this is not the case, because a protected dataset X0 can be used to construct good classifiers for future data. To do so, we describe an extensive set of experiments that we have run with different SDC protection methods and different (real) datasets. In general, the resulting classifiers are very good, which is good news for both the SDC and the Privacy-preserving Data Mining communities. In particular, our results question the necessity of some specific protection methods that have appeared in the\ud privacy-preserving data mining (PPDM) literature with the clear goal of providing good classification