Cloud computing is a recent trend of technology that aims to provide unlimited, on-demand, elastic computing and data storage resources. In this context, cloud services decrease the need for local data storage and the infrastructure costs. However, hosting confidential data at a cloud storage service requires the transfer of control of the data to a semi-trusted external provider. Therefore, data confidentiality is the top concern from the cloud issues list. Recently, three main approaches have been introduced to ensure data confidentiality in cloud services: data encryption; combination of encryption and fragmentation; and fragmentation. Besides, other strategies use a mix of these three main approaches. In this paper, we present i-OBJECT, a new approach to preserve data confidentiality in cloud environments. The proposed mechanism uses information decomposition to split data into unrecognizable parts and store them in different cloud service providers. Experimental results show the potential efficiency of i-OBJECT.

Time dependent networks are of key importance to allow computing precise travel times taking into consideration moving object's departure time. However the computation of time functions that are used to annotate time dependent networks are challenging since we must cope with noisy and incomplete traffic data. Recent related works adopt approaches that build Piecewise linear functions, which do not cope with aforementioned problems. In this work, we propose a new method for generating Piecewise linear functions by applying a map-matching technique allied to a curve smoothing approach in order to treat outliers and complete data. We performed experiments using real trajectory data and compared our results with a baseline. Preliminary results show that our approach generates time functions with better approximation than the baseline competitor.

In this paper we propose a new kind of kNN query on time-dependent network, which aims at finding k points of interest that are closest in time to a query point. This query is useful for many kind of applications where a user/customer should ask for a service provided by many moving providers (e.g. Taxi drivers, ambulances, food delivers, etc). We described our solution and present experimental results comparing our proposed algorithm to a baseline approach. The experimental results show that our approach is efficient and effective.

This work encompasses the development of a new method for classifying tax fraudsters based on fraud indicators. This work was developed in conjunction with a Brazilian fiscal agency aim at avoiding fiscal evasion. The main contribution of this paper is a method that allows classifying and ranking taxpayers analyzing fraud indicators obtained from several fiscal applications. Particularly, we developed a method for identifying frequent fraud patterns using association rules and then we apply two dimension reduction methods (i.e. PCA and SVD) in order to create a fraud scale, which allows ranking taxpayers according to their potential to commit a fraud. Experiments were conducted using real taxpayer data. Tax auditors, specialized in fraud detection, validated our results. Preliminary results show that our method may indicate fraudsters with 80% of accuracy, which is definitely an excellent result.