Towards a Collection of Security and Privacy Patterns

Security and privacy (SP)-related challenges constitute a significant barrier to the wider adoption of Internet of Things (IoT)/Industrial IoT (IIoT) devices and the associated novel applications and services. In this context, patterns, which are constructs encoding re-usable solutions to common problems and building blocks to architectures, can be an asset in alleviating said barrier. More specifically, patterns can be used to encode dependencies between SP properties of individual smart objects and corresponding properties of orchestrations (compositions) involving them, facilitating the design of IoT solutions that are secure and privacy-aware by design. Motivated by the above, this work presents a survey and taxonomy of SP patterns towards the creation of a usable pattern collection. The aim is to enable decomposition of higher-level properties to more specific ones, matching them to relevant patterns, while also creating a comprehensive overview of security- and privacy-related properties and sub-properties that are of interest in IoT/IIoT environments. To this end, the identified patterns are organized using a hierarchical taxonomy that allows their classification based on provided property, context, and generality, while also showing the relationships between them. The two high-level properties, Security and Privacy, are decomposed to a first layer of lower-level sub-properties such as confidentiality and anonymity. The lower layers of the taxonomy, then, include implementation-level enablers. The coverage that these patterns offer in terms of the considered properties, data states (data in transit, at rest, and in process), and platform connectivity cases (within the same IoT platform and across different IoT platforms) is also highlighted. Furthermore, pointers to extensions of the pattern collection to include additional patterns and properties, including Dependability and Interoperability, are given. Finally, to showcase the use of the presented pattern collection, a practical application is detailed, involving the pattern-driven composition of IoT/IIoT orchestrations with SP property guarantees

Clinical validation of a public health policy-making platform for hearing loss (EVOTION): protocol for a big data study

INTRODUCTION: The holistic management of hearing loss (HL) requires an understanding of factors that predict hearing aid (HA) use and benefit beyond the acoustics of listening environments. Although several predictors have been identified, no study has explored the role of audiological, cognitive, behavioural and physiological data nor has any study collected real-time HA data. This study will collect ‘big data’, including retrospective HA logging data, prospective clinical data and real-time data via smart HAs, a mobile application and biosensors. The main objective is to enable the validation of the EVOTION platform as a public health policy-making tool for HL. METHODS AND ANALYSIS: This will be a big data international multicentre study consisting of retrospective and prospective data collection. Existing data from approximately 35 000 HA users will be extracted from clinical repositories in the UK and Denmark. For the prospective data collection, 1260 HA candidates will be recruited across four clinics in the UK and Greece. Participants will complete a battery of audiological and other assessments (measures of patient-reported HA benefit, mood, cognition, quality of life). Patients will be offered smart HAs and a mobile phone application and a subset will also be given wearable biosensors, to enable the collection of dynamic real-life HA usage data. Big data analytics will be used to detect correlations between contextualised HA usage and effectiveness, and different factors and comorbidities affecting HL, with a view to informing public health decision-making. ETHICS AND DISSEMINATION: Ethical approval was received from the London South East Research Ethics Committee (17/LO/0789), the Hippokrateion Hospital Ethics Committee (1847) and the Athens Medical Center’s Ethics Committee (KM140670). Results will be disseminated through national and international events in Greece and the UK, scientific journals, newsletters, magazines and social media. Target audiences include HA users, clinicians, policy-makers and the general public. TRIAL REGISTRATION NUMBER: NCT03316287; Pre-results

Healthier and Independent Living of the Elderly: Interoperability in a Cross-Project Pilot

The ageing of the population creates new heterogeneous challenges for age-friendly living. The progressive decline in physical and cognitive skills tends to prevent elderly people from performing basic instrumental activities of daily living and there is a growing interest in technology for aging support. Digital health today can be exercised by anyone owning a smartphone and parameters such as heart rate, step counts, calorie intake, sleep quality, can be collected and used not only to monitor and improve the individual’s health condition but also to prevent illnesses. However, for the benefits of e-health to take place, digital health data, either Electronic Health Records (EHR) or sensor data from the IoMT, must be shared, maintaining privacy and security requirements but unlocking the potential for research an innovation throughout EU. This paper demonstrates the added value of such interoperability requirements, and a form of accomplishing them through a cross-project pilot

Reference architectures, platforms, and pilots for european smart and healthy living—analysis and comparison

Motivated by the aging trend, much effort is being invested into implementing ICT (Information and Communications Technology)-enabled systems to provide a better quality of life and support the independent living of older people. As a result, many systems, often labeled as eHealth or AAL (Ambient/Active Assisted Living), were developed over the years. In creating such systems, which very often serve various needs, different architectures have emerged. This work focuses on analyzing and comparing the work and architectures from seven (six of which are in progress) EU-funded healthcare projects, with a total budget of 126MEUR in which we participate. After establishing the theoretical foundation by defining core concepts, we give a brief background on architectures in eHealth and AAL. We elaborate on the chosen analysis method based on three established healthcare and AAL taxonomies we identified by performing a literature survey and the selected Reference Architecture Model (RAM). Since there is no standard way of describing architectures in the eHealth and AAL domain, we conducted the online survey during August and September 2020 and identified CREATE-IoT 3D RAM as the most appropriate option. We present a classification of selected projects based on established taxonomies and map projects’ architectures to CREATE-IoT 3D RAM, which we also propose as standard RAM for future digital healthcare and AAL projects. During our analysis, we identify the most common types of assistance: communication support, reminders, monitoring, and guidance to address health and communication issues. We conclude that proper ecosystems are critical for lowering entry barriers and facilitating sustainable solutions for smart and healthy living

Influence of water uptake on the aerosol particle light scattering coefficients of the Central European aerosol

The influence of aerosol water uptake on the aerosol particle light scattering was examined at the regional continental research site Melpitz, Germany. The scattering enhancement factor f(RH), defined as the aerosol particle scattering coefficient at a certain relative humidity (RH) divided by its dry value, was measured using a humidified nephelometer. The chemical composition and other microphysical properties were measured in parallel. f(RH) showed a strong variation, e.g. with values between 1.2 and 3.6 at RH=85% and λ=550 nm. The chemical composition was found to be the main factor determining the magnitude of f(RH), since the magnitude of f(RH) clearly correlated with the inorganic mass fraction measured by an aerosol mass spectrometer (AMS). Hysteresis within the recorded humidograms was observed and explained by long-range transported sea salt. A closure study using Mie theory showed the consistency of the measured parameters

Study of the relative humidity dependence of aerosol light-scattering in southern Spain

This investigation focuses on the characterisation of the aerosol particle hygroscopicity. Aerosol particle optical properties were measured at Granada, Spain, during winter and spring seasons in 2013. Measured optical properties included particle light-absorption coefficient (sap) and particle light-scattering coefficient (ssp) at dry conditions and at relative humidity (RH) of 85 +/- 10%. The scattering enhancement factor, f(RH=85%), had a mean value of 1.5 +/- 0.2 and 1.6 +/- 0.3 for winter and spring campaigns, respectively. Cases of high scattering enhancement were more frequent during the spring campaign with 27% of the f(RH=85%) values above 1.8, while during the winter campaign only 8% of the data were above 1.8. A Saharan dust event (SDE), which occurred during the spring campaign, was characterised by a predominance of large particles with low hygroscopicity. For the day when the SDE was more intense, a mean daily value of f(RH=85%)=1.3 +/- 0.2 was calculated. f(RH=85%) diurnal cycle showed two minima during the morning and afternoon traffic rush hours due to the increase in non-hygroscopic particles such as black carbon and road dust. This was confirmed by small values of the single-scattering albedo and the scattering Angstrom exponent. A significant correlation between f(RH=85%) and the fraction of particulate organic matter and sulphate was obtained. Finally, the impact of ambient RH in the aerosol radiative forcing was found to be very small due to the low ambient RH. For high RH values, the hygroscopic effect should be taken into account since the aerosol forcing efficiency changed from -13W/m2 at dry conditions to -17W/m2 at RH=85%.This work was supported by the Andalusia Regional Government through projects P10-RNM-6299 and P12-RNM-2409; by the Spanish Ministry of Economy and Competitiveness through projects CGL2010-18782, CSD2007-00067, CGL2011-13580-E/CLI and CGL2011-16124-E; and by EU through ACTRIS project (EU INFRA-2010-1.1.16-262254).G. Titos was funded by the program FPI of the Spanish Ministry of Economy and Competitiveness – Secretariat of Science, Innovation and Development under grant BES-2011-043721

Speech audiometry test with picture-related sentence lists in Modern Greek for partially hearing children

Objective: The aim of this study was to develop Greek sentence-based speech audiometry test in quiet test for hearing-impaired (HI) children (G-SEBSAT). Methods: Seventy-six children were recruited following approval by the local ethics committee and after obtaining informed consent from their parents. The collection of vocabulary was based on showing pictures selected from popular reading materials in Greek to HI children. A grammatical content analysis was carried out to determine the average syntactic and morphological structures of the sentences used by the HI children. Ten picture-related sentence lists were developed based on the vocabulary and the grammatical analysis, and recorded by a male native speaker of standard Modern Greek. These were presented to both normal-hearing (NH) and HI children, and the average speech response threshold (SRT) as well as the slope of the SRT curve at the SRT level of 50% correct responses (S50) were recorded in both groups. Sentence lists were validated with respect to the variability of their difficulty within each group, as well the test-retest variability of the respective SRT scores. Results: The average SRT across all lists for HI children was 65.27 dB and the slope of the SRT curve at the SRT level of 50% correct responses was 3.11%/dB. The corresponding results across all lists for NH children were 17.66 dB and 9.7%/dB, respectively. The SRT of HI children were strongly positively correlated, in a statistically significant manner with the pure tone audiogram (PTA) in both the test and the retest sessions (test: r = 0.750, p <.0005; retest: r = 0.753, p <.0005). The Spearman’s correlation of the rankings of SRT values and the slope values was 0.998 and 0.997, respectively, for the HI and 0.939 and 0.88, for the NH group, indicating very low variability across the test and retest sessions. In addition, the analysis of variance (ANOVA) of the average SRT in NH children and the SRT residuals in the HI group indicated that the different sentences were of the same difficulty within each group. ((F(9,81) = 0.401, p =.930 and (F(9,93) = 2.241, p =.025, respectively). Conclusions: A validated G-SEBSAT was created in Greek for the first time. SRT and S50 values for both NH and HI children are comparable to similar tests developed in other languages. © 2017 International Association of Physicians in Audiology

Clinical validation of a public health policy-making platform for hearing loss (EVOTION): Protocol for a big data study

The holistic management of hearing loss (HL) requires an understanding of factors that predict hearing aid (HA) use and benefit beyond the acoustics of listening environments. Although several predictors have been identified, no study has explored the role of audiological, cognitive, behavioural and physiological data nor has any study collected real-time HA data. This study will collect big data', including retrospective HA logging data, prospective clinical data and real-time data via smart HAs, a mobile application and biosensors. The main objective is to enable the validation of the EVOTION platform as a public health policy-making tool for HL. Methods and analysis This will be a big data international multicentre study consisting of retrospective and prospective data collection. Existing data from approximately 35 000 HA users will be extracted from clinical repositories in the UK and Denmark. For the prospective data collection, 1260 HA candidates will be recruited across four clinics in the UK and Greece. Participants will complete a battery of audiological and other assessments (measures of patient-reported HA benefit, mood, cognition, quality of life). Patients will be offered smart HAs and a mobile phone application and a subset will also be given wearable biosensors, to enable the collection of dynamic real-life HA usage data. Big data analytics will be used to detect correlations between contextualised HA usage and effectiveness, and different factors and comorbidities affecting HL, with a view to informing public health decision-making. Ethics and dissemination Ethical approval was received from the London South East Research Ethics Committee (17/LO/0789), the Hippokrateion Hospital Ethics Committee (1847) and the Athens Medical Center's Ethics Committee (KM140670). Results will be disseminated through national and international events in Greece and the UK, scientific journals, newsletters, magazines and social media. Target audiences include HA users, clinicians, policy-makers and the general public. Trial registration number NCT03316287; Pre-results. © Article author(s) 2018

Using Big Data to Develop a Clinical Decision Support System for Tinnitus Treatment

Tinnitus is a common symptom of a phantom sound perception with a considerable socioeconomic impact. Tinnitus pathophysiology is enigmatic and its significant heterogeneity reflects a wide spectrum of clinical manifestations, severity and annoyance among tinnitus sufferers. Although several interventions have been suggested, currently there is no universally accepted treatment. Moreover, there is no well-established correlation between tinnitus features or patients’ characteristics and projection of treatment response. At the clinical level, this practically means that selection of treatment is not based on expected outcomes for the particular patient. The complexity of tinnitus and lack of well-adapted prognostic factors for treatment selection highlight a potential role for a decision support system (DSS). A DSS is an informative system, based on big data that aims to facilitate decision-making based on: specific rules, retrospective data reflecting results, patient profiling and predictive models. Therefore, it can use algorithms evaluating numerous parameters and indicate the weight of their contribution to the final outcome. This means that DSS can provide additional information, exceeding the typical questions of superiority of one treatment versus another, commonly addressed in literature. The development of a DSS for tinnitus treatment selection will make use of an underlying database consisting of medical, epidemiological, audiological, electrophysiological, genetic and tinnitus subtyping data. Algorithms will be developed with the use of machine learning and data mining techniques. Based on the profile features identified as prognostic these algorithms will be able to suggest whether additional examinations are needed for a robust result as well as which treatment or combination of treatments is optimal for every patient in a personalized level. In this manuscript we carefully define the conceptual basis for a tinnitus treatment selection DSS. We describe the big data set and the knowledge base on which the DSS will be based and the algorithms that will be used for prognosis and treatment selection. © 2021, Springer Nature Switzerland AG