Papers by DOMINICK MAURICIO GARCIA SANCHEZ
Association rules have been widely used in many application areas to extract from raw data new an... more Association rules have been widely used in many application areas to extract from raw data new and useful information expressed in a comprehensive way for decision makers. Nowadays, with the increase of the volume and the variety of data, the classical data mining workflow is showing insufficient. We can expect in the near future that, more often than not, several mining processes will be carried out over the same or different sources, thus requiring extracted information to be fused in order to provide a unified, not overwhelming view to the user. In this paper we propose a new technique for fusing associations rules. The notion of meta-association rule is introduced for that purpose. Meta-association rules are association rules where the antecedent or the consequent can contain regular rules that have been previously extracted with a high reliability in a high percentage of the source databases. We illustrate out proposal with an example in the domain of crime data analysis.
2013 11th Annual Conference on Privacy, Security and Trust, PST 2013, 2013
Web search engines (WSEs) build user profiles and use them to offer an enhanced web search experi... more Web search engines (WSEs) build user profiles and use them to offer an enhanced web search experience. Nevertheless, these elements might contain sensitive data that may represent a privacy threat for the users. There are some works in the literature that address this situation while preserving the profile usefulness. These schemes submit synthetic queries that are fake but related to the real general interests of the user. Specifically, they rely on past user queries to obtain the legitimate interests of each user. We argue that this is not always the best strategy and, in this paper, we study the use of social networks to gather this information and provide a better personalized service while offering an equivalent privacy level.
2006 IEEE International Conference on Fuzzy Systems, 2006
Although "texture" is one of the most used features in image analysis, it is an ambiguous concept... more Although "texture" is one of the most used features in image analysis, it is an ambiguous concept which, in many cases, is not easy to characterize. In this paper we face the problem of imprecision in texture description by proposing a methodology to represent texture concepts by means of fuzzy sets. Specifically, we model the concept of "coarseness", the most extended in texture analysis, relating representative measures of this kind of texture (usually some statistic) with its presence degree. To obtain these "presence degrees" related to human perception, we propose a methodology to collect assessments from polls filled by human subjects, performing an aggregation of these assessments by means of OWA operators. Using as reference set a combination of some statistics, the membership function corresponding to the fuzzy set "coarseness" will be modelled as the function which provides the best fit of the collected data. The proposed methodology could be extended to other types of texture concepts like orientation, roughness or regularity. The main novelty of this approach is the introduction of semantics in the texture analysis problem, by using linguistic labels represented by fuzzy sets in order to describe texture features.
Three main components of experience base in linguistic description of data
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013
ABSTRACT In this paper, we present a contribution to solve the problem of organizing the represen... more ABSTRACT In this paper, we present a contribution to solve the problem of organizing the representation of experience in computational systems which are able to generate relevant linguistic descriptions of data for specific users and contexts. We claim, that, typically, the expert knowledge modeled in these systems is limited to one of the dimensions of the meaning of natural language. Here, we model the experience base distinguishing among three types of meaning, namely, Ideational meaning concerning with the technical, impersonal description of the specific phenomenon; Interpersonal meaning concerning with the role of the partners involved in the communication process and Textual meaning concerning with the contribution to the meaning of the specific realization with natural language of both previous types of meaning. In order to organize these types of meaning (also called components of experience base) in a practical computational representation, we have built an ontology that will help designers to model their experience in the application domain. Using this ontology, the computational system is able of identifying the most suitable linguistic descriptions for describing the input data. Our approach is presented with the support of a practical example in the domain of the maintenance of comfort in a room.
2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2013
A common view in some data anonymization literature is to oppose the "old" k-anonymity model to t... more A common view in some data anonymization literature is to oppose the "old" k-anonymity model to the "new" differential privacy model, which offers more robust privacy guarantees. However, the utility of the masked results provided by differential privacy is usually limited, due to the amount of noise that needs to be added to the output, or because utility can only be guaranteed for a restricted type of queries. This is in contrast with the general-purpose anonymized data resulting from k-anonymity mechanisms, which also focus on preserving data utility. In this paper, we show that a synergy between differential privacy and k-anonymity can be found when the objective is to release anonymized data: k-anonymity can help improving the utility of the differentially private release. Specifically, we show that the amount of noise required to fulfill ε-differential privacy can be reduced if noise is added to a k-anonymous version of the data set, where k-anonymity is reached through a specially designed microaggregation of all attributes. As a result of noise reduction, the analytical utility of the anonymized output data set is increased. The theoretical benefits of our proposal are illustrated in a practical setting with an empirical evaluation on a reference data set.
Hierarchical Genetic Algorithm for Type-2 fuzzy Integration applied to Human Recognition
2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013
In this paper a new model of a Hierarchical Genetic Algorithm (HGA) for fuzzy inference system op... more In this paper a new model of a Hierarchical Genetic Algorithm (HGA) for fuzzy inference system optimization is proposed. The proposed HGA optimizes the fuzzy integrators architecture (type of system, number of trapezoidal membership functions, and their parameters). The model was applied to pattern recognition based on the iris, ear and voice biometrics. Fuzzy logic is used as a method for modular neural networks (MNNs) response integration.
IEEE Transactions on Information Forensics and Security, 2013
The advent of new information sharing technologies has led the society to a scenario where thousa... more The advent of new information sharing technologies has led the society to a scenario where thousands of textual documents are publicly published every day. The existence of confidential information in many of these documents motivates the use of measures to hide sensitive data before being published, which is precisely the goal of document sanitization. Even though methods to assist the sanitization process have been proposed, most of them are focused on the detection of specific types of sensitive entities for concrete domains, lacking generality and requiring from user supervision. Moreover, to hide sensitive terms, most approaches opt by removing them; a measure that hampers the utility of the sanitized document. This paper presents a general-purpose sanitization method that, based on information theory and exploiting knowledge bases, detects and hides sensitive textual information while preserving its meaning. Our proposal works in an automatic and unsupervised way and it can be applied to heterogeneous documents, which make it specially suitable for environments with massive and heterogeneous information sharing needs. Evaluation results show that our method outperforms strategies based on trained classifiers regarding the detection recall, whereas it better retains document's utility compared to term suppression methods.
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Papers by DOMINICK MAURICIO GARCIA SANCHEZ