Patient data sharing for the improvement of healthcare and medical research: towards a moral duty of patients to share health-related data?

The project answers to the following central research question: ‘How would a moral duty of patients to transfer (health) data for the benefit of health care improvement, research, and public health in the eHealth sector sit within the existing confidentiality, privacy, and data protection legislations?’. The improvement of healthcare services, research, and public health relies on patient data, which is why one might raise the question concerning a potential moral responsibility of patients to transfer data concerning health. Such a responsibility logically would have subsequent consequences for care providers concerning the further transferring of health data with other healthcare providers or researchers and other organisations (who also possibly transfer the data further with others and other organisations). Otherwise, the purpose of the patients’ moral duty, i.e. to improve the care system and research, would be undermined. Albeit the arguments that may exist in favour of a moral responsibility of patients to share health-related data, there are also some moral hurdles that come with such a moral responsibility. Furthermore, the existing European and national confidentiality, privacy and data protection legislations appear to hamper such a possible moral duty, and they may need to be reconsidered to unlock the full use of data for healthcare and research.

Temperature Variation Aware Energy Optimization in Heterogeneous MPSoCs

Thermal effects are rapidly gaining importance in nanometer heterogeneous integrated systems. Increased power density, coupled with spatio-temporal variability of chip workload, cause lateral and vertical temperature non-uniformities (variations) in the chip structure. The assumption of an uniform temperature for a large circuit leads to inaccurate determination of key design parameters. To improve design quality, we need precise estimation of temperature at detailed spatial resolution which is very computationally intensive. Consequently, thermal analysis of the designs needs to be done at multiple levels of granularity. To further investigate the flow of chip/package thermal analysis we exploit the Intel Single Chip Cloud Computer (SCC) and propose a methodology for calibration of SCC on-die temperature sensors. We also develop an infrastructure for online monitoring of SCC temperature sensor readings and SCC power consumption. Having the thermal simulation tool in hand, we propose MiMAPT, an approach for analyzing delay, power and temperature in digital integrated circuits. MiMAPT integrates seamlessly into industrial Front-end and Back-end chip design flows. It accounts for temperature non-uniformities and self-heating while performing analysis. Furthermore, we extend the temperature variation aware analysis of designs to 3D MPSoCs with Wide-I/O DRAM. We improve the DRAM refresh power by considering the lateral and vertical temperature variations in the 3D structure and adapting the per-DRAM-bank refresh period accordingly. We develop an advanced virtual platform which models the performance, power, and thermal behavior of a 3D-integrated MPSoC with Wide-I/O DRAMs in detail. Moving towards real-world multi-core heterogeneous SoC designs, a reconfigurable heterogeneous platform (ZYNQ) is exploited to further study the performance and energy efficiency of various CPU-accelerator data sharing methods in heterogeneous hardware architectures. A complete hardware accelerator featuring clusters of OpenRISC CPUs, with dynamic address remapping capability is built and verified on a real hardware.

Surveillance risks in IoT applied to smart cities

Nowadays, cities deal with unprecedented pollution and overpopulation problems, and Internet of Things (IoT) technologies are supporting them in facing these issues and becoming increasingly smart. IoT sensors embedded in public infrastructure can provide granular data on the urban environment, and help public authorities to make their cities more sustainable and efficient. Nonetheless, this pervasive data collection also raises high surveillance risks, jeopardizing privacy and data protection rights. Against this backdrop, this thesis addresses how IoT surveillance technologies can be implemented in a legally compliant and ethically acceptable fashion in smart cities. An interdisciplinary approach is embraced to investigate this question, combining doctrinal legal research (on privacy, data protection, criminal procedure) with insights from philosophy, governance, and urban studies. The fundamental normative argument of this work is that surveillance constitutes a necessary feature of modern information societies. Nonetheless, as the complexity of surveillance phenomena increases, there emerges a need to develop more fine-attuned proportionality assessments to ensure a legitimate implementation of monitoring technologies. This research tackles this gap from different perspectives, analyzing the EU data protection legislation and the United States and European case law on privacy expectations and surveillance. Specifically, a coherent multi-factor test assessing privacy expectations in public IoT environments and a surveillance taxonomy are proposed to inform proportionality assessments of surveillance initiatives in smart cities. These insights are also applied to four use cases: facial recognition technologies, drones, environmental policing, and smart nudging. Lastly, the investigation examines competing data governance models in the digital domain and the smart city, reviewing the EU upcoming data governance framework. It is argued that, despite the stated policy goals, the balance of interests may often favor corporate strategies in data sharing, to the detriment of common good uses of data in the urban context.

Anatomical biomarkers of body composition in oncological population: application and development of artificial intelligences

In the Era of precision medicine and big medical data sharing, it is necessary to solve the work-flow of digital radiological big data in a productive and effective way. In particular, nowadays, it is possible to extract information “hidden” in digital images, in order to create diagnostic algorithms helping clinicians to set up more personalized therapies, which are in particular targets of modern oncological medicine. Digital images generated by the patient have a “texture” structure that is not visible but encrypted; it is “hidden” because it cannot be recognized by sight alone. Thanks to artificial intelligence, pre- and post-processing software and generation of mathematical calculation algorithms, we could perform a classification based on non-visible data contained in radiological images. Being able to calculate the volume of tissue body composition could lead to creating clasterized classes of patients inserted in standard morphological reference tables, based on human anatomy distinguished by gender and age, and maybe in future also by race. Furthermore, the branch of “morpho-radiology" is a useful modality to solve problems regarding personalized therapies, which is particularly needed in the oncological field. Actually oncological therapies are no longer based on generic drugs but on target personalized therapy. The lack of gender and age therapies table could be filled thanks to morpho-radiology data analysis application.

Neo-commodification of persons: exploitation of personal data and impact on the sharing economy

The notion of commodification is a fascinating one. It entails many facets, ranging from subjective debates on desirability of commodification to in depth economic analyses of objects of value and their corresponding markets. Commodity theory is therefore not just defined by a single debate, but spans a plethora of different discussions. This thesis maps and situates those theories and debates and selects one specific strain to investigate further. This thesis argues that commodity theory in its optima forma deals with the investigation into what sets commodities apart from non-commodities. It proceeds to examine the many given answers to this question by scholars ranging from the mid 1800’s to the late 2000’s. Ultimately, commodification is defined as a process in which an object becomes an element of the total wealth of societies in which the capitalist mode of production prevails. In doing so, objects must meet observables, or indicia, of commodification provided by commodity theories. Problems arise when objects are clearly part of the total wealth in societies without meeting established commodity indicia. In such cases, objects are part of the total wealth of a society without counting as a commodity. This thesis examines this phenomenon in relation to the novel commodities of audiences and data. It explains how these non-commodities (according to classical theories) are still essential elements of industry. The thesis then takes a deep dive into commodity theory using the theory on the construction of social reality by John Searle.

Indagini forensi in tema di scambio di file pedopornografici mediante software di file sharing a mezzo peer-to-peer

La prova informatica richiede l’adozione di precauzioni come in un qualsiasi altro accertamento scientifico. Si fornisce una panoramica sugli aspetti metodologici e applicativi dell’informatica forense alla luce del recente standard ISO/IEC 27037:2012 in tema di trattamento del reperto informatico nelle fasi di identificazione, raccolta, acquisizione e conservazione del dato digitale. Tali metodologie si attengono scrupolosamente alle esigenze di integrità e autenticità richieste dalle norme in materia di informatica forense, in particolare della Legge 48/2008 di ratifica della Convenzione di Budapest sul Cybercrime. In merito al reato di pedopornografia si offre una rassegna della normativa comunitaria e nazionale, ponendo l’enfasi sugli aspetti rilevanti ai fini dell’analisi forense. Rilevato che il file sharing su reti peer-to-peer è il canale sul quale maggiormente si concentra lo scambio di materiale illecito, si fornisce una panoramica dei protocolli e dei sistemi maggiormente diffusi, ponendo enfasi sulla rete eDonkey e il software eMule che trovano ampia diffusione tra gli utenti italiani. Si accenna alle problematiche che si incontrano nelle attività di indagine e di repressione del fenomeno, di competenza delle forze di polizia, per poi concentrarsi e fornire il contributo rilevante in tema di analisi forensi di sistemi informatici sequestrati a soggetti indagati (o imputati) di reato di pedopornografia: la progettazione e l’implementazione di eMuleForensic consente di svolgere in maniera estremamente precisa e rapida le operazioni di analisi degli eventi che si verificano utilizzando il software di file sharing eMule; il software è disponibile sia in rete all’url http://www.emuleforensic.com, sia come tool all’interno della distribuzione forense DEFT. Infine si fornisce una proposta di protocollo operativo per l’analisi forense di sistemi informatici coinvolti in indagini forensi di pedopornografia.

Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources.