Abstract As society experiences accelerated ageing, understanding the complex biological processes of human ageing, which are affected by a large number of variables and factors, becomes increasingly crucial. Artificial intelligence (AI) presents a promising avenue for ageing research, offering the ability to detect patterns, make accurate predictions, and extract valuable insights from large volumes of complex, heterogeneous data. As ageing research increasingly leverages AI techniques, we present a timely systematic literature review to explore the current state-of-the-art in this field following a rigorous and transparent review methodology. As a result, a total of 77 articles have been identified, summarised, and categorised based on their characteristics. AI techniques, such as machine learning and deep learning, have been extensively used to analyse diverse datasets, comprising imaging, genetic, behavioural, and contextual data. Findings showcase the potential of AI in predicting age-related outcomes, developing ageing biomarkers, and determining factors associated with healthy ageing. However, challenges related to data quality, interpretability of AI models, and privacy and ethical considerations have also been identified. Despite the advancements, novel approaches suggest that there is still room for improvement to provide personalised AI-driven healthcare services and promote active ageing initiatives with the ultimate goal of enhancing the quality of life and well-being of older adults. Graphical abstract Overview of the literature review.

The proper exploitation of vast amounts of event data by means of process mining techniques enables the discovery, monitoring and improvement of business processes, allowing organizations to develop more efficient business intelligence systems. However, event data often contain personal and/or confidential information that, unless properly managed, may jeopardize people’s privacy while conducting process mining analysis. Despite its relevance, privacy aspects have barely been considered within process mining, and the field of privacy-preserving process mining is still in an embryonic stage. With the aim to protect people’s privacy, this article presents a novel privacy-preserving process mining method based on microaggregation techniques, called k-PPPM, that increases privacy in process mining through k-anonymity. Contrary to current solutions, mostly based on pseudonyms and encryption, this method averts the re-identification of targeted individuals from attacks based on the analysis of process models in combination with location-oriented attacks, such as Restricted Space Identification and Object Identification attacks. The proposed method provides adjustable parameters to tune different anonymization aspects. Six real-life event logs have been employed to evaluate the method in terms of process models quality and information loss.

The understanding of human ageing contributes to the overall improvement of healthcare and opens the door to the increase of life expectancy and quality of life. Studies on human ageing, which are evidence-based, require data. Although some efforts have been put to concentrate datasets in single reference points, many datasets remain hidden in articles, studies and research projects websites.In this paper, we aim at identifying those datasets that have been used for the study of human ageing and make them easy to find to researchers in the field. To do so, we have analysed well-known literature databases, previous reviews, and specialised sources. We have reviewed the available data and, as a result, we have identified and discussed 28 datasets. Hence, this article provides an organised reference point to datasets for researchers interested in the study of human ageing and contributes to their visibility.

The growing interest in healthy ageing has fostered the definition and use of proper measures to assess decline in older adults. The World Health Organisation has defined the concept of intrinsic capacity to address this issue. In this paper, we approach the intrinsic capacity concept from an engineering perspective. Our contribution is threefold: (i) we summarise the results of a literature review on the topic, which points to a lack of holistic solutions to assess intrinsic capacity automatically, by using information technology, (ii) we suggest a data-warehouse-inspired architecture to tackle the problem, and (iii) we discuss the main challenges that remain open and must be studied in the future. Overall, this paper is a first step towards the definition of an architecture allowing the practical implementation of a holistic, context-aware, automatic system to measure intrinsic capacity and monitor the healthy ageing of elders.

The COVID19 pandemic situation has opened a wide range of opportunities for cyber-criminals, who take advantage of the anxiety generated and the time spent on the Internet, to undertake massive phishing campaigns. Although companies are adopting protective measures, the psychological traits of the victims are still considered from a very generic perspective. In particular, current literature determines that the model proposed in the Big-Five personality traits (i.e., Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) might play an important role in human behaviour to counter cybercrime. However, results do not provide unanimity regarding the correlation between phishing susceptibility and neuroticism. With the aim to understand this lack of consensus, this article provides a comprehensive literature review of papers extracted from relevant databases (IEEE Xplore, Scopus, ACM Digital Library, and Web of Science). Our results show that there is not a well-established psychological theory explaining the role of neuroticism in the phishing context. We sustain that non-representative samples and the lack of homogeneity amongst the studies might be the culprits behind this lack of consensus on the role of neuroticism on phishing susceptibility.

The Human Genome Project has generated a great wealth of information. Currently, almost all human genome has been sequenced and now it is time to identify the functionality of each gene. The sequence of base pairs accounts for approximately 3 billion elements. While there are many efficient algorithms and implementations to mine this information, doing it privately is a great challenge. Current state-of-the-art methods have improved their efficiency, but they are not practical yet. In this article, we introduce several protocols to drastically boost the performance of genome mining processes while guaranteeing privacy, thus, enabling practical implementations. We describe how to solve the private set intersection problem and a set of pattern matching queries with privacy. The proposed protocols are server-assisted and we prove that they are secure under the semi-honest model. We report the assessment of our solution using synthetic datasets and prove their efficiency.

A key aspect of online social networks (OSNs) is the user-generated multimedia content shared online. OSNs like Facebook have to deal with up to 300 million photos uploaded on a daily basis, both video- and audio-related social networks have also started to gain important shares of the market. Although the security and privacy mechanisms deployed by OSNs can cope with several risks and discourage inexperienced users from malicious behaviours, many issues still need to be addressed. Uploaded multimedia content carries information that could be transmitted virally and almost instantaneously within OSNs and beyond. OSNs could be seen as a multimedia heaven for users. However, in many cases they might end up being the user's personal hell with information disclosure or distortion, contrary to his/her will. In this article, we outline the most significant security and privacy issues related to the exposure of multimedia content in OSNs and we discuss possible countermeasures.

In remote storage services, delays in the time to retrieve data can cause economic losses to the data owners. In this paper, we address the problem of properly establishing specific clauses in the service level agreement (SLA), intended to guarantee a short and predictable retrieval time. Based on the rationale that the retrieval time mainly depends on the storage media used at the server side, we introduce the concept of Provable Storage Medium (PSM), to denote the ability of a user to efficiently verify that the provider is complying to this aspect of the SLA. We propose PSM as an extension of Provable Data Possession (PDP): embedding challenge-response PDP schemes with measurements of the response time, both properties can be enforced without any need for the user to locally store nor download her data. We describe a realistic implementation of PSM in a scenario where data should be stored both in RAM and HDD. A thorough analysis shows that, even for relatively small challenges, the total time to compute and deliver the response is sensibly affected by the remarkable difference in the access time of the two supports. An extensive simulation campaign confirms the quality and viability of our proposal.

The quality of life has been significantly improved and one of the main reasons is the medical advances of the past decades. Nevertheless, to further advance the research and services in the field, practitioners, researchers and health organizations should share more information. While this need is indisputable, the sensitivity of the information demands that it is preprocessed, so that the published data are anonym zed and individuals cannot be identified. The scope of this work is to highlight the difficulties in providing automated anonymization approaches for medical records without consulting experts in the field. One of the major problems that is going to be highlighted is that Quasi-Identifiers (QI) are not independent. It is well known that combinations of QIs can be used to infer other relevant information. Nevertheless, this work tries to exploit the other way of information flow, we show how sensitive attributes can be exploited to derive information about the QIs, leading to many privacy hazards for the patients whose records are shared. To this extent, we illustrate some relevant examples and discuss probable counter-measures.

Segmentation of the breast region is usually the first step in the analysis of mammograms. Due to the non-uniformity of the background, breast segmentation presents several difficulties especially for film based mammograms. Our experimental results show that 50% of digitized film based mammograms in the mini-MIAS database do not have uniform intensity in the background. For this reason, applying a global thresholding method produces inaccurate results. In addition, finding the optimal global threshold value by only using histogram information requires a reliable objective function that characterizes the statistics of the background and the mammogram regions in the digitized mammograms. A second way to find the boundary of the breast consists in fitting a deformable model, such as snakes, on the mammogram. However, this method has three main shortcomings. First, the model must be initialized near the boundary. Second, using gradient information in the objective function can push the boundary toward the tissues inside the breast rather than the actual boundary. Third, in some mammograms the breast region is occluded by artifacts, such as labels, that have high gradient values on their boundary and cause the deformable model to be fitted on the artifact. To address these problems we propose a probabilistic adaptive thresholding method that uses texture information and its probability to find the most probable threshold values for specific parts of the mammogram. The experimental results on mini-MIAS database show that our proposed method outperforms the state-of-art methods and improves the accuracy at least 37% in comparison with the best results obtained by contour growing methods.

Age-related diseases are becoming more prominent due to life expectancy increase in developed countries. Mild cognitive impairment and several types of dementia like Alzheimer's disease are gaining importance both socially and economically. Patients suffering from these diseases have different degrees of autonomy and, thus, different needs. Often, relatives or friends take care of those patients. However, during the first stages of the disease, they still have a high degree of autonomy and frown on the supervision of others. Despite their autonomy, patients could get lost and disoriented. Rapidly determining the location of a lost patient is paramount to reduce the risk of suffering serious injuries. Current solutions to this problem are based on the continuous monitoring of the patient. Such continuous control might be seen by most people as a privacy invasion, and it may discourage patients from using these solutions. In this article we present the concept of m-Carer as a smart mobile device able to privately monitor the movements of patients having diverse degrees of mobility and autonomy. After justifying the need for privacy-aware m-carers due to social and economical reasons, we propose a complete architecture aimed at fulfilling the needs of patients, relatives and healthcare services. Moreover, we show a real implementation of our proposal so as to confirm that it is technically sound and feasible.

In this paper, we present the concept of double-phase microaggregation as an improvement of classical microaggregation for the protection of privacy in distributed scenarios without fully trusted parties. We apply this new concept in the context of mobile health and we show that a distributed architecture consisting of patients and several intermediate entities can apply it to protect the privacy of patients, whose data are released to third parties for secondary use. After recalling some fundamental concepts of statistical disclosure control and microaggregation, we detail the distributed architecture that allows the private gathering, storage, and sharing of biomedical data. We show that double-phase multivariate microaggregation properly fits the needs for privacy preservation of biomedical data in the distributed context of mobile health. Moreover, we show that double-phase microaggregation performs similarly to classical microaggregation in terms of information loss, disclosure risk, and correlation preservation, while avoiding the limitations of a centralized approach.

A new multisensory system that aims at fostering the interaction of people with cerebral palsy is presented. This article describes the strategies and technologies used to provide people who have moderate to severe cerebral palsy with playful and fun activities designed according to their abilities. These activities are based on interactive systems that use computer vision and generate graphics and sounds in real time. The well-being that is achieved through the use of these activities is the result of gaining a significant degree of autonomy by the users. The presented system was first developed in the Cerebral Palsy Centre of Tarragona, Spain. Its motivation came from the low rate of users able to interact with computers. Although several assistive technology gadgets and special software applications (e.g., cause–effect and educational activities, simple navigation environments, etc.) were used, most users simply did not understand the interaction mechanisms. It was thought that a highly interactive activity (reinforced with sounds and images closely related with gestures) would be more accessible to most users in spite of their sensory, motor, and cognitive impairments. Tests with impaired users show that the proposal promotes participation, engagement, and play. In this article, the experimental methodology, the used and developed tools, and the results that were obtained are explained.

Microaggregation is a Statistical Disclosure Control (SDC) technique that aims at protecting the privacy of individual respondents before their data are released. Optimally microaggregating multivariate data sets is known to be an NP-hard problem. Thus, using heuristics has been suggested as a possible strategy to tackle it. Specifically, Genetic Algorithms have been shown to be serious candidates that can find good solutions on small data sets. However, due to the very nature of these algorithms and the coding of the microaggregation problem, GA can hardly cope with large data sets. In order to apply them to large data sets, the latter have to be previously partitioned into smaller disjoint subsets that the GA can handle. In this article we summarise several proposals for partitioning data sets, in order to use GA to microaggregate them. In addition, we suggest a new partitioning strategy based on the variable-MDAV algorithm, and we compare it with the most relevant previous proposals. The experimental results show that our method outperforms the previous ones in terms of information loss.

Protecting personal data is essential to guarantee the rule of law1. Due to the new Information and Communication Technologies (ICTs) unprecedented amounts of personal data can be stored and analysed. Thus, if the proper measures are not taken, individual privacy could be in jeopardy. Being the aim to protect individual privacy, a great variety of statistical disclosure control (SDC) techniques has been proposed. Amongst many others, k-anonymity is a promising property that, if properly achieved, can help protect individual privacy. In this paper, we propose a new post-processing method that can be applied after a k-anonymity algorithm, being the aim to lessen the errors resulting from the aggregation of data. We show that our method can be extended to work with many other SDC techniques and we provide some experimental results which emphasise the usefulness of our proposal.

This paper is the continuation of a series of related work about experimentation of alternative ways of interaction with computers for disabled people (concretely with users suffering from cerebral palsy). In order to define an effective methodology to evaluate the usability and/or accessibility levels for this user profile, here, we start studying and evaluating (a) different interactive devices and interaction techniques that enable the use of computers for this people and, (b) existing metric techniques from human-computer interaction field. With this basis the article proposes a new evaluation method that simplifies the existing ones reducing the complexity to the minimum. This proposal is extensively explained and field research will soon be started

Blocking is a well-known technique used to partition a set of records into several subsets of manageable size. The standard approach to blocking is to split the records according to the values of one or several attributes (called blocking attributes). This paper presents a new blocking method based on 2/sup d/-trees for intelligently partitioning very large data sets for micro aggregation. A number of experiments has been carried out in order to compare our method with the most typical univariate one.

This work presents a new algorithm for determining the trajectory of a mobile robot and, simultaneously, creating a detailed volumetric 3D model of its workspace. The algorithm exclusively utilizes information provided by a single stereo vision system, avoiding thus the use both of more costly laser systems and error-prone odometry. Six-degrees-of-freedom egomotion is directly estimated from images acquired at relatively close positions along the robot's path. Thus, the algorithm can deal with both planar and uneven terrain in a natural way, without requiring extra processing stages or additional orientation sensors. The 3D model is based on an octree that encapsulates clouds of 3D points obtained through stereo vision, which are integrated after each egomotion stage. Every point has three spatial coordinates referred to a single frame, as well as true-color components. The spatial location of those points is continuously improved as new images are acquired and integrated into the model.

This paper proposes a new coordination algorithm for efficiently exploring an unknown environment with a team of mobile robots. The proposed technique subsequently applies a well-known unsupervised clustering algorithm (k-means) in order to fairly divide the remaining unknown space into as many disjoint regions as available robots. Each robot is primarily responsible for exploring its assigned region and can help other robots on its way through. Unknown space is dynamically repartitioned as new areas are discovered by the team, balancing thus the overall workload among team members and naturally leading to greater dispersion over the environment and thus faster broad coverage than with previous greedy-like approaches, which guide robots based on maximum profit strategies that simply trade off between distance to the closest frontiers and amount of unknown cells likely to be discovered from them.