The widespread availability of pseudonymized user datasets has enabled personalized recommendation systems. However, recent studies have shown that users can be de-anonymized by exploiting the uniqueness of their data patterns, raising significant privacy concerns. This paper presents a novel approach that tackles the challenging task of linking user identities across multiple rating datasets from diverse domains, such as movies, books, and music, by leveraging the consistency of users’ rating patterns as high-dimensional quasi-identifiers. The proposed method combines probabilistic record linkage techniques with quasi-identifier attacks, employing the Fellegi–Sunter model to compute the likelihood of two records referring to the same user based on the similarity of their rating vectors. Through extensive experiments on three publicly available rating datasets, we demonstrate the effectiveness of the proposed approach in achieving high precision and recall in cross-dataset de-anonymization tasks, outperforming existing techniques, with F1-scores ranging from 0.72 to 0.79 for pairwise de-anonymization tasks. The novelty of this research lies in the unique integration of record linkage techniques with quasi-identifier attacks, enabling the effective exploitation of the uniqueness of rating patterns as high-dimensional quasi-identifiers to link user identities across diverse datasets, addressing a limitation of existing methodologies. We thoroughly investigate the impact of various factors, including similarity metrics, dataset combinations, data sparsity, and user demographics, on the de-anonymization performance. This work highlights the potential privacy risks associated with the release of anonymized user data across diverse contexts and underscores the critical need for stronger anonymization techniques and tailored privacy-preserving mechanisms for rating datasets and recommender systems.

Light curve analysis usually involves extracting manually designed features associated with physical parameters and visual inspection. The large amount of data collected nowadays in astronomy by different surveys represents a major challenge of characterizing these signals. Therefore, finding good informative representation for them is a key non-trivial task. Some studies have tried unsupervised machine learning approaches to generate this representation without much effectiveness. In this article, we show that variational auto-encoders can learn these representations by taking the difference between successive timestamps as an additional input. We present two versions of such auto-encoders: Variational Recurrent Auto-Encoder plus time (VRAE${}_t$) and re-Scaling Variational Recurrent Auto Encoder plus time (S-VRAE${}_t$). The objective is to achieve the most likely low-dimensional representation of the time series that matched latent variables and, in order to reconstruct it, should compactly contain the pattern information. In addition, the S-VRAE${}_t$ embeds the re-scaling preprocessing of the time series into the model in order to use the Flux standard deviation in the learning of the light curves structure. To assess our approach, we used the largest transit light curve dataset obtained during the 4 years of the Kepler mission and compared to similar techniques in signal processing and light curves. The results show that the proposed methods obtain improvements in terms of the quality of the deep representation of phase-folded transit light curves with respect to their deterministic counterparts. Specifically, they present a good balance between the reconstruction task and the smoothness of the curve, validated with the root mean squared error, mean absolute error, and auto-correlation metrics. Furthermore, there was a good disentanglement in the representation, as validated by the Pearson correlation and mutual information metrics. Finally, a useful representation to distinguish categories was validated with the $F_1$ score in the task of classifying exoplanets. Moreover, the S-VRAE${}_t$ model increases all the advantages of VRAE${}_t$, achieving a classification performance quite close to its maximum model capacity and generating light curves that are visually comparable to a Mandel–Agol fit. Thus, the proposed methods present a new way of analyzing and characterizing light curves.

Multiple models have been proposed to describe the epidemic spreading in the presence of interactions between two or more infectious diseases, but less is known about how dynamical aspects, such as time scales of diseases, affect the epidemic spreading. In this work, we evaluate the time shift produced in the number of people infected from one disease when interacting with another disease. Using a compartmental model, we produce different forms of relationship as competition, cooperation, and independence, assessing the effect of each one in the final result. We focus on the case of the unidirectional coupling between diseases, which enables us to study the impact of a perturbation to a driving disease on the driven one. We found that the prevalence of the driven disease is strongly affected if its time scale, defined by the time where the infection reaches the peak, is comparable to that of the driving disease. The secondary peak of the infection was observed under cooperative coupling if the time scale of the driving disease is much longer than that of the driven one.