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Josep Domingo-Ferrer, Antoni Martínez-Ballesté, Josep Maria Mateo-Sanz, Francesc Sebé

The VLDB Journal 15 (4), 355-369

Description: Microaggregation is a family of methods for statistical disclosure control (SDC) of microdata (records on individuals and/or companies), that is, for masking microdata so that they can be released while preserving the privacy of the underlying individuals. The principle of microaggregation is to aggregate orig- inal database records into small groups prior to pub- lication. Each group should contain at least k records to prevent disclosure of individual information, where k is a constant value preset by the data protector. Re- cently, microaggregation has been shown to be useful to achieve k-anonymity, in addition to it being a good masking method. Optimal microaggregation (with mini- mum within-groups variability loss) can be computed in polynomial time for univariate data. Unfortunately, for multivariate data it is an NP-hard problem. Several heu- ristic approaches to microaggregation have been pro- posed in the literature. Heuristics yielding groups with fixed size k tends to be more efficient, whereas data- oriented heuristics yielding variable group size tends to result in lower information loss. This paper presents new data-oriented heuristics which improve on the trade-off between computational complexity and information loss and are thus usable for large datasets.

Fran Casino, Constantinos Patsakis, Antoni Martinez-Balleste, Frederic Borras, Edgar Batista

arXiv preprint arXiv:1706.04109,

Description: In this article, we explain in detail the internal structures and databases of a smart health application. Moreover, we describe how to generate a statistically sound synthetic dataset using real-world medical data.

Abdulrahman Al-Molegi, Mohammed Jabreel, Antoni Martinez-Balleste

Pattern Recognition Letters 112, 34-40

Description: Predicting people’s next location has attracted the attention of both scientists and large Internet com- panies, for a variety of reasons. The analysis of location data collected from smart mobile devices paves the way for the improvement of current location based services and the rise of new business models, based on rich notifications related to the right prediction of users’ next location. Moreover, the so-called attention technique has been adopted in neural networks learning, aiming at considering alignments between different parts of the source training data. This article proposes the model Move, Attend and Predict (MAP) to predict a person’s future location based on his/her mobility pattern col- lected by a mobile device. This is achieved by means of a computationally efficient trainable deep neural network model. Our model essentially learns which time interval in the trajectory sequences are relevant regarding a specific location. In order to extract the meaningful representation from tra- jectories and time sequences, the embedding representation learning technique is used. Experimental results, obtained from tests conducted on two large real-life datasets, demonstrate that our model out- performs state-of-the-art models in terms of precision, recall and F1-score performance metrics.

Abdulrahman Al-Molegi, Izzat Alsmadi, Antoni Martínez-Ballesté

Pervasive and Mobile Computing 47, 31-53

Description: Location-Based Services (LBSs) provide users especially in smartphones with information and services based on their location and interests. Predicting people’s next location could help in improving the quality of such services and, in turn, boosting people’s confidence on those services. Any successful LBS algorithm or model should target three major goals: Location predic- tion accuracy, high throughput or fast response and efficiency in terms of utilizing smartphone resources. This paper proposes a new approach to dis- cover and predict people’s next location based on their mobility patterns, while being computationally efficient. The approach starts by discovering Regions-of-Interest (RoIs) in people’s historical trajectories (which denote the locations where users have been previously, frequently). A new model based on Markov Chain (MC) is proposed to overcome the drawback of clas- sical MC. Our proposed model considers both space and time contexts where the space represents a specific location that has been visited by a user while the time represents the location visiting time. We show how classical MC model can be extended to include movement times and how time will im- prove prediction accuracy. One Unique finding in our research is related to the value of integrating users’ mobility location/space with time context. In particular, time context is formulated in a way to add extra information to the space context. For better abstraction during building the model, a general transformation function is used to transform the n-order MC into first order. Results show that the proposed approach provides significant improvements in predicting people’s next location compared with the state- of-the-art models when applied on two large real-life datasets, GeoLife and Gowalla.

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