Albanese M. (2013) Measuring Trust in Big Data. In: Aversa R., Kołodziej J., Zhang J., Amato F., Fortino G. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8286. Springer, Cham
The huge technological progress we have witnessed in the last decade has enabled us to generate data at an unprecedented rate, leading to what has become the era of big data. However, big data is not just about generating, storing, and retrieving massive amounts of data. The focus should rather be on new analytical approaches that would enable us to extract actionable intelligence from this ocean of data. From a security standpoint, one of the main issues that need to be addressed is the trustworthiness of each source or piece of information. In this paper, we propose an approach to assess and quantify the trust level of both information sources and information items. Our approach leverages the vast literature on citation ranking, and we clearly show the benefits of adapting citation ranking mechanisms to this new domain, both in terms of scalability and in terms of quality of the results.