Where you go is who you are -- A study on machine learning based semantic privacy attacks

Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On top of that, machine learning methods provide new powerful tools to interpret big data. In light of these considerations, we raise the following question: What is the actual risk that realistic, machine learning based privacy attacks can obtain meaningful semantic information from raw location data, subject to inaccuracies in the data? In response, we present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information, leading to a significant privacy loss even with location inaccuracy of up to 200m. With location obfuscation of more than 1 km, spatial information hardly adds any value, but a high privacy risk solely from temporal information remains. The availability of public context data such as POIs plays a key role in inference based on spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data, which policymakers should consider for privacy regulations, and which could guide individuals in their personal location protection measures

Scriabin Sonata-Fantasy op. 19 n. 2 on Record: A Comparative Study of Sound Recordings and Piano Rolls

Since the advent of the record, more than forty recordings of the Scriabin Sonata-Fantasy op. 19 n. 2 have been produced, as well as two piano rolls, with Igumnov (1911) and the composer himself performing the work (1910). This thesis sets out to analyse the evolution of performance style of this work within the last two centuries, using numerical values and music analysis software, in search of a verifiable analysis of performance traits. This study aims to detect strategies and techniques that performers of the work have used to form their performances. Observation is oriented to register long-scale and short-scale performance details, which are equally valuable in one’s preparation when practising a musical work. The actual sound of the sonata has been primarily assessed. The sonata is secondarily viewed as music text (Belaieff edition) and simultaneously compared to Scriabin’s own recorded performance on piano roll, which is valid only to a certain extent, due to recording technical impediments. The final goal of this research is to bring to light some neglected or merely underrated pianistic techniques, so as to inform the contemporary performer on different possibilities of expression. This experimentation could result in a richer musical language

Ενσυναίσθηση και ικανότητα αναγνώρισης των συναισθηματικών εκφράσεων του προσώπου σε άτομα με ψυχική διαταραχή και επιθετική συμπεριφορά

Η παρούσα έρευνα σχεδιάστηκε, προκειμένου να διερευνήσει τη σχέση μεταξύ της ενσυναίσθησης, της αλεξιθυμίας και της αναγνώρισης των συναισθηματικών εκφράσεων του προσώπου σε άτομα με ψυχική ασθένεια και επιθετική συμπεριφορά. Το δείγμα της μελέτης αποτελούνταν αποκλειστικά από άντρες. Επιλέχθηκαν τριάντα δύο ψυχικά πάσχοντες με σχιζοφρένεια και προηγούμενο καταγεγραμμένο ποινικό ιστορικό καθώς και τριάντα δύο άτομα υγιούς πληθυσμού που ορίστηκαν ως ομάδα ελέγχου. Χορηγήθηκαν το ερωτηματολόγιο της Επιθετικότητας, η Δοκιμασία Αναγνώρισης Συναισθημάτων του Προσώπου, η Κλίμακα Αλεξιθυμίας του Toronto, το Ερωτηματολόγιο για το Πηλίκο της Ενσυναίσθησης και η υποκλίμακα ενσυναίσθητο ενδιαφέρον ή σύμφωνη θυμική ενσυναίσθηση της Κλίμακας Διαπροσωπικής Ανταπόκρισης, προκειμένου να εξεταστούν τα παραπάνω γνωρίσματα. Αναμένονταν ότι οι ψυχικά πάσχοντες θα διέφεραν σημαντικά σε σχέση με την ομάδα ελέγχου στα εξεταζόμενα χαρακτηριστικά και συγκεκριμένα θα σημείωναν περισσότερη επιθετικότητα και αλεξιθυμία, μικρότερη ενσυναίσθηση καθώς και μεγαλύτερη δυσκολία να αναγνωρίσουν τις συναισθηματικές εκφράσεις του προσώπου. Παράλληλα, αναμένονταν ότι τα χαρακτηριστικά αυτά συνδυασμένα ανά δύο θα βρίσκονταν σε μια δυναμική σχέση μεταξύ τους. Ακόμη, τέθηκε προς διερεύνηση η ικανότητα των ψυχικά πασχόντων να αναγνωρίζουν ευκολότερα τα θετικά συναισθήματα έναντι των αρνητικών. Συμπερασματικά, φάνηκε ότι τα ευρήματα της έρευνας παρείχαν πλήρης υποστήριξη στην πρώτη υπόθεση, τεκμηριώνοντας μερικώς τις άλλες δύο.The aim of the current study is to investigate the relationship among empathy, alexithymia and recognition of facial expressions in patients with mental illness and violent behavior. The research sample consists exclusively of males. Thirty-two patients with schizophrenia and recorded criminal history were chosen to participate in this study as well as thirty-two healthy men defined as control group. A multi-method approach was administered to evaluate these characteristics (Aggression Questionnaire, Pictures of facial affect, Toronto Alexithymia Scale, Empathy Quotient and empathic concern factor from Interpersonal Reactivity Scale). It was expected that patients with schizophrenia would have more aggression as well as more alexithymia, less empathy and greater difficulty to recognize facial emotion expressions compared to control group. Simultaneously, it was anticipated that these features combined in two would be in a dynamic relationship with each other. Furthermore, it was investigated whether patients could easily recognize positive emotions compared to negative ones. In conclusion, it appeared that the findings of the investigation provided full support to the first hypothesis, supporting partially the other tw

Computers, Environment and Urban Systems / Adaptive areal elimination (AAE) : a transparent way of disclosing protected spatial datasets

Geographical masking is the conventional solution to protect the privacy of individuals involved in confidential spatial point datasets. The masking process displaces confidential locations to protect individual privacy while maintaining a fine level of spatial resolution. The adaptive form of this process aims to further minimize the displacement error by taking into account the underlying population density. We describe an alternative adaptive geomasking method, referred to as Adaptive Areal Elimination (AAE). AAE creates areas of a minimum K-anonymity and then original points are either randomly perturbed within the areas or aggregated to the median centers of the areas. In addition to the masked points, K-anonymized areas can be safely disclosed as well without increasing the risk of re-identification. Using a burglary dataset from Vienna, AAE is compared with an existing adaptive geographical mask, the donut mask. The masking methods are evaluated for preserving a predefined K-anonymity and the spatial characteristics of the original points. The spatial characteristics are assessed with four measures of spatial error: displaced distance, correlation coefficient of density surfaces, hotspots' divergence, and clusters' specificity. Masked points from point aggregation of AAE have the highest spatial error in all the measures but the displaced distance. In contrast, masked points from the donut mask are displaced the least, preserve the original spatial clusters better, have the highest clusters' specificity and correlation coefficient of density surfaces. However, when the donut mask is adapted to achieve an actual K-anonymity, the random perturbation of AAE introduces less spatial error than the donut mask for all the measures of spatial error.(VLID)231721

Developing a Citizen Social Science approach to understand urban stress and promote wellbeing in urban communities

This paper sets out the future potential and challenges for developing an interdisciplinary, mixed-method Citizen Social Science approach to researching urban emotions. It focuses on urban stress, which is increasingly noted as a global mental health challenge facing both urbanised and rapidly urbanising societies. The paper reviews the existing use of mobile psychophysiological or biosensing within urban environments—as means of ‘capturing’ the urban geographies of emotions. Methodological reflections are included on primary research using biosensing in a study of workplace and commuter stress for university employees in Birmingham (UK) and Salzburg (Austria) for illustrative purposes. In comparing perspectives on the conceptualisation and measurement of urban stress from psychology, neuroscience and urban planning, the difficulties of defining scientific constructs within Citizen Science are discussed to set out the groundwork for fostering interdisciplinary dialogue. The novel methods, geo-located sensor technologies and data-driven approaches to researching urban stress now available to researchers pose a number of ethical, political and conceptual challenges around defining and measuring emotions, stress, human behaviour and urban space. They also raise issues of rigour, participation and social scientific interpretation. Introducing methods informed by more critical Citizen Social Science perspectives can temper overly individualised forms of data collection to establish more effective ways of addressing urban stress and promoting wellbeing in urban communities

Quality assessment of OpenStreetMap data using trajectory mining

OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigation