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Politik und Firmen nutzen zunehmend Microtargeting. Dazu werden gezielt ausgewählte Gruppen von Menschen mit auf sie abgestimmten Nachrichten angesprochen. Dies geschieht meist über soziale Netzwerke und Internetnachrichtenkanäle. Ein Microtargeting durch psychometrische Analyse großer Datensätze (Big Data) scheint besonders in Wahlkämpfen mit knappen Mehrheitsverhältnissen relevant, um Wählermeinungen zu beeinflussen. Potenzielle Anhänger sollen überzeugt, Wähler von Konkurrenten verunsichert und demotiviert werden. Bekannt und populär wurde das Microtargeting im US-Wahlkampf 2008 von Barack Obama und wird seitdem von Demokraten und Republikanern gleichermaßen in den Wahlkämpfen angewendet. Risiken des Microtargetings werden darin gesehen, dass kleine Einheiten der Gesellschaft mit für sie passenden Nachrichten adressiert werden. Die hierdurch in diesen Gruppen entstehenden Filterblasen würden einen freien Diskurs unmöglich machen. Ein weiteres Risiko besteht in der Ansprache von potenziellen Käufern mit sehr gezielter Werbung zu kommerziellen Zwecken. Besondere Aufmerksamkeit hat das Thema Ende 2016 durch einen Artikel in der Schweizer Zeitschrift »Magazin« gewonnen. Der Artikel berichtet über die britische Firma Cambridge Analytica, die maßgeblichen Einfluss auf den Wahlsieg von Trump und den Brexit gehabt haben soll. Anhand von Facebooklikes in Verbindung mit weiteren Daten, u. a. demografische Informationen, wurden präzise Wählerprofile von Einzelpersonen erstellt und diese mit politischen Botschaften adressiert. Nach der Veröffentlichung des Artikels in der Schweizer Zeitschrift wurden jedoch Stimmen laut, die den tatsächlichen Einfluss dieser Aktivitäten grundsätzlich infrage stellten. Auch Mitarbeiter der Firma haben mittlerweile die Wirkungen stark relativiert. Die tatsächliche Einflussmöglichkeit des Microtargetings im Bereich Politik und Wirtschaft ist daher derzeit ungewiss. Ferner stellt sich die Frage, inwieweit der Einsatz des Microtargetings vom deutschen und europäischen Datenschutz geregelt ...

In the United States today, there is no single law to address the privacy concerns associated with the collection of consumer data. Lawmakers have introduced policies that seek to address data privacy at the federal level, but Congress has not yet acted to create a comprehensive law to protect consumers. On the contrary, in 2016, the European Union passed its General Data Protection Regulation to address the dangers associated with "Big Data" and to give consumers control over their data. Unfortunately, in the United States consumers are often unaware of how their data is being handled and what is done with their data once a security breach has occurred. In Kaufman v. Google LLC, for instance, Ronnie Kaufman filed a class action lawsuit against Google for its alleged deceptive practices of tracking and storing location data after users apparently deactivated Google's ability to track and store this data. According to the complaint, Google represented to the public and its users that it would not access user location history if users took certain steps in managing their privacy settings. Unfortunately, however, Kaufman alleged that Google continued to track and store her personal data. This note analyzes the implications of the Big Data Era on individual privacy rights in the United States. It argues that companies should write "opt-out" privacy policies in a clear and comprehensible manner, so that consumers are completely aware of the ways in which personal data is being collected. If used correctly, big data is extremely beneficial to a functional society and to the business world. Yet, to preserve big data's benefits, the United States must stop falling behind in its regulation.

International audience ; The control of public procurement is a relatively traditional field of investigation for audit institutions. However, it was considered complex and time-consuming and may not have been given all the necessary attention. New tools, based on Big Data, significantly increase the transparency of practices in this area. They can profoundly renew the vision of control institutions, civil society and buyers alike. ; Le contrôle de la commande publique est champ d'investigation relativement classique pour les juridictions financières. Il était toutefois réputé complexe et chronophage et pouvait ne pas faire l'objet de toutes les attentions nécessaires. Les nouveaux outils, fondés sur le big data, accroissent de manière significative la transparence des pratiques en la matière. Ils peuvent renouveler en profondeur la vision des institutions de contrôles, de la société civile, comme des acheteurs.

International audience ; The control of public procurement is a relatively traditional field of investigation for audit institutions. However, it was considered complex and time-consuming and may not have been given all the necessary attention. New tools, based on Big Data, significantly increase the transparency of practices in this area. They can profoundly renew the vision of control institutions, civil society and buyers alike. ; Le contrôle de la commande publique est champ d'investigation relativement classique pour les juridictions financières. Il était toutefois réputé complexe et chronophage et pouvait ne pas faire l'objet de toutes les attentions nécessaires. Les nouveaux outils, fondés sur le big data, accroissent de manière significative la transparence des pratiques en la matière. Ils peuvent renouveler en profondeur la vision des institutions de contrôles, de la société civile, comme des acheteurs.

International audience ; The control of public procurement is a relatively traditional field of investigation for audit institutions. However, it was considered complex and time-consuming and may not have been given all the necessary attention. New tools, based on Big Data, significantly increase the transparency of practices in this area. They can profoundly renew the vision of control institutions, civil society and buyers alike. ; Le contrôle de la commande publique est champ d'investigation relativement classique pour les juridictions financières. Il était toutefois réputé complexe et chronophage et pouvait ne pas faire l'objet de toutes les attentions nécessaires. Les nouveaux outils, fondés sur le big data, accroissent de manière significative la transparence des pratiques en la matière. Ils peuvent renouveler en profondeur la vision des institutions de contrôles, de la société civile, comme des acheteurs.

International audience ; The control of public procurement is a relatively traditional field of investigation for audit institutions. However, it was considered complex and time-consuming and may not have been given all the necessary attention. New tools, based on Big Data, significantly increase the transparency of practices in this area. They can profoundly renew the vision of control institutions, civil society and buyers alike. ; Le contrôle de la commande publique est champ d'investigation relativement classique pour les juridictions financières. Il était toutefois réputé complexe et chronophage et pouvait ne pas faire l'objet de toutes les attentions nécessaires. Les nouveaux outils, fondés sur le big data, accroissent de manière significative la transparence des pratiques en la matière. Ils peuvent renouveler en profondeur la vision des institutions de contrôles, de la société civile, comme des acheteurs.

International audience ; The control of public procurement is a relatively traditional field of investigation for audit institutions. However, it was considered complex and time-consuming and may not have been given all the necessary attention. New tools, based on Big Data, significantly increase the transparency of practices in this area. They can profoundly renew the vision of control institutions, civil society and buyers alike. ; Le contrôle de la commande publique est champ d'investigation relativement classique pour les juridictions financières. Il était toutefois réputé complexe et chronophage et pouvait ne pas faire l'objet de toutes les attentions nécessaires. Les nouveaux outils, fondés sur le big data, accroissent de manière significative la transparence des pratiques en la matière. Ils peuvent renouveler en profondeur la vision des institutions de contrôles, de la société civile, comme des acheteurs.

'informazione costituisce, attualmente, una delle principali risorse economiche del processo produttivo e, di conseguenza, le attività di raccolta, selezione e monitoraggio dei dati personali assumono una fondamentale rilevanza poichè rivolte alla capillare targettizzazione e fidelizzazione del consumatore. In questa direzione, la privacy, da tutela individualistica del diritto ad essere lasciati soli, assume il significato di diritto al pieno controllo delle informazioni personali. L'analisi dei Big Data si spinge sino alla ricerca e all'esame di ogni possibile correlazione tra i dati e gli algoritmi impiegati nei processi decisionali. Se l'uso cooperativo e partecipato nella sfera pubblica di alcune tipologie di Big Data può rivestire un sicuro interesse sociale, in altri casi l'utilizzo di tali conoscenze solleva notevoli criticità concernenti la tutela dei dati personali, il loro sfruttamento economico, la presenza di un'effettiva consapevolezza e libertà nella manifestazione del consenso al loro trattamento. Ulteriori incognite, conseguite all'impiego dei Big Data, sono rappresentate dalla polarizzazione dell'informazione in capo a pochi intermediari digitali che palesa tutta l'asimmetria tra chi offre il servizio informativo e il suo fruitore, aggravata dall'intrasparenza e dalla selettività dei criteri posti alla base del funzionamento dell'algoritmo, problematiche che si riflettono sulla piena esplicazione dei diritti di libertà e sul futuro della democrazia. Si mostra, allora, tutta l'urgenza di un'efficace regolazione dei Big Data e, più in generale, delle informazioni personali circolanti online, ispirata ai valori costituzionalmente garantiti e diretta alla tutela dell'individuo dall'impiego improprio delle tecnologie informatiche, nella prospettiva di un'innovativa delineazione di modelli di cittadinanza digitale attiva, a fondamento di un'effettiva libertà di costruzione personale. ; Information is currently one of the main economic resources of the production process and, consequently, the collection, selection and monitoring of personal data assume a fundamental importance as they are aimed at capillary consumer targeting and retention. In this direction, privacy, from individualistic protection of the right to be left alone, takes on the meaning of the right to full control of personal information. The Big Data analysis goes as far as searching and examining any possible correlation between the data and the algorithms used in the decision-making processes. If the cooperative and participatory use in the public sphere of some types of Big Data can be of certain social interest, in other cases the use of such knowledge raises significant critical issues concerning the protection of personal data, their economic exploitation, the presence of an effective awareness and freedom in the manifestation of consent to their treatment. Further unknowns, resulting from the use of Big Data, are represented by the polarization of information by a few digital intermediaries that reveals all the asymmetry between those who offer the information service and its user, aggravated by the transparency and the selectivity of the criteria places at the base of the functioning of the algorithm, problems that are reflected on the full explanation of the rights of freedom and on the future of democracy. All the urgency of an effective regulation of Big Data and, more generally, of personal information circulating online, inspired by the constitutionally guaranteed values and directed to the protection of the individual from the improper use of information technology, is shown in the the prospect of an innovative delineation of models of active digital citizenship which are the foundation of an effective freedom of personal construction.

The maritime industry has become a major part of globalization. Political and economic actors are meeting challenges regarding shipping and people transport. The Automatic Identification System (AIS) records and broadcasts the location of numerous vessels and delivers a huge amount of information that can be used to analyze fluxes and behaviors. However, the exploitation of these numerous messages requires tools based on Big Data principles. Acknowledgement of origin, destination, travel duration and distance of each vessel can help transporters to manage their fleet and ports to analyze fluxes and focus their investigations on some containers based on their previous locations. Thanks to the historical AIS messages provided by the Danish Maritime Authority and ARLAS PROC/ML, an open source and scalable processing platform based on Apache SPARK, we are able to apply our pipeline of processes and extract this information from millions of AIS messages. We use a Hidden Markov Model (HMM) to identify when a vessel is still or moving and we create "courses", embodying the travel of the vessel. Then we derive the travel indicators. The visualization of results is made possible by ARLAS Exploration, an open source and scalable tool to explore geolocated data. This carto-centered application allows users to navigate into the huge amount of enriched data and helps to take benefits of these new origin and destination indicators. This tool can also be used to help in the creation of Machine Learning algorithms in order to deal with many maritime transportation challenges.

As housing-related decisions are increasingly being made by algorithms instead of individuals, it is critical that the technologies used to make those decisions do not replicate or even worsen patterns of discrimination and segregation. While it may be convenient to believe that bias can be eliminated by putting decision-making authority in the hands of machines instead of people, studies have shown that technologies such as algorithms and machine learning are often infected with bias. Provisions of the Fair Housing Act ("FHA") and its accompanying regulations that protect individuals from discriminatory algorithms are under attack from the Department of Housing and Urban Development ("HUD"), the agency responsible for enforcing the FHA. In particular, HUD recently issued a proposed rule that, if enacted, would undermine disparate impact jurisprudence and specifically exempt many housing providers who rely on algorithms developed by third parties. With the FHA under attack from the agency charged with its enforcement, it is particularly important to study how technological advancements might be used to either improve or undermine the law's effectiveness. This article describes the advent of big data, algorithmic decision-making, and machine learning, as well as HUD's recent proposal to specifically immunize housing providers who rely on algorithms from disparate impact liability. It then discusses how the use of big data and algorithmic decision-making has touched all parts of the rental housing market, from advertising to tenant selection processes. Finally, it offers policy prescriptions that could help mitigate the discriminatory impacts of algorithmic decision-making in ways that are aligned with the FHA or, in some cases, that reach further than the protections currently offered under the FHA.

When in 1966 the United Nations stated in its International Covenant on Civil and Political Rights, the ideal of a free human being with respect to his privacy through the prohibition of arbitrary interference in his private life, it was not possible to imagine the impact of global unlimited connectivity, autonomy of new information technologies, the development of huge interconnected databases, the independent and unrestricted circulation of data, which have led to ethical and legal questions arising from this to treat personal and health data. ; Cuando en el año 1966 Naciones Unidas planteaba en su Declaración Internacional de los Derechos Civiles y Políticos, el ideal de un ser humano libre con respeto a su intimidad a través de la prohibición de injerencias arbitrarias en su vida privada, no era posible imaginar el impacto global que tendría la irrupción de una conectividad casi ilimitada, la autonomía de las nuevas tecnologías de la información, el desarrollo de enormes bases de datos interconectadas, la circulación independiente e irrestricta de datos, que han generado interrogantes éticos y jurídicos derivados de esta forma de tratar los datos personales y de salud.

The widespread diffusion of the digital culture and technology, involving both individual and population, together with the fast pacing digital globalization process (far surpassing 'political' globalization), is radically changing the world social landscape, including medicine and clinical research. The most significant change in clinical research is the ever more frequent acceptance of observational data, both through the use of registry of rare or common conditions and the implementation of capillary networks recording the daily clinical practice (Electronic Health Recording system). By becoming 'observational' clinical practice should change significantly: (i) record of different data (epidemiological, clinical, and administrative) in inter-operational database, producing a dynamic map of the health demands, either met or not, that allows a reconfiguration of the health systems capable to adapt to the shifting clinical needs. Implicate the larger group possible of patients and healthy individuals, who, through smartphone technology, could participate in primary and secondary prevention projects and epidemiological analyses. (ii) Support scientific research by integrating it with the clinical practice as instrument of good government that is scientific evidence-based Public Health System: the Learning Health System. The road will be long and gruelling. A first negative by-product is the proliferation of cybercrime throughout digital medicine.

European Union's Transport policy's pivotal aim is to strengthen the existing Transport infrastructure, which is crucial to economic development. The improvement in the transport sector should provide efficient logistics of goods, better travel and commuting facilities, and accessibility of the European region. This report, as part of the first phase of the Leveraging Big Data to Manage Transport Operations (LeMO) project, provides an introduction to big data in the transport sector. It identifies untapped opportunities and challenges and describes numerous data sources. This report is a part of WP1 which is a cornerstone of the LeMO project. It aims to generate a shared understanding of current big data landscape in transport and identifies a holistic view on opportunities, challenges, and limitations. The remainder of this report is structured as follows: Chapter 2 explores the characteristic of big data and highlights the big data challenges in the transport sector. It covers six transportation modes (air, rail, road, urban, water and multi-modal) and two transportation sectors (passenger and freight). Chapter 3 identifies several opportunities and challenges of big data in transportation, by using: several subject matter expert interviews, nineteen applied cases, and a literature review. It also indicates that the combination of different means and approaches will enhance the opportunities for successful big data services in the transport sector. Chapter 4 offers an intensive survey of the various data sources, data producers, and service providers. In addition, cartography was modeled to visualize data flows intuitively. Cartography demonstrates where data originated from and where it is flowing to. Chapter 5 summarizes all findings and provides a conclusion.

[Resumen] Cuando en el año 1966 Naciones Unidas planteaba en su Declaración Internacional de los Derechos Civiles y Políticos, el ideal de un ser humano libre con respeto a su intimidad a través de la prohibición de injerencias arbitrarias en su vida privada, no era posible imaginar el impacto global que tendría la irrupción de una conectividad casi ilimitada, la autonomía de las nuevas tecnologías de la información, el desarrollo de enormes bases de datos interconectadas, la circulación independiente e irrestricta de datos, que han generado interrogantes éticos y jurídicos derivados de esta forma de tratar los datos personales y de salud. ; [Abstract] When in 1966 the United Nations stated in its International Covenant on Civil and Political Rights, the ideal of a free human being with respect to his privacy through the prohibition of arbitrary interference in his private life, it was not possible to imagine the impact of global unlimited connectivity, autonomy of new information technologies, the development of huge interconnected databases, the independent and unrestricted circulation of data, which have led to ethical and legal questions arising from this to treat personal and health data

The NATO STO Centre for Maritime Research and Experimentation, as part of its mission to put forward the technological maritime research, with the support of the European Union's Horizon 2020 Programme has organized on May 9-10, 2018, the Maritime Big Data Workshop (MBDW). The workshop gathered together researchers, technological providers and members of the operational community to exchange their experience on Big Data innovations for maritime security, safety and security of maritime navigation and transport, sustainable fisheries and exploitation of ocean resources. For two days, 37 researchers and experts from Brazil, Canada, France, Germany, Greece, Italy, Portugal, South Africa, and Vietnam presented their work and main findings on Maritime and Big Data, including the outcomes of 6 ongoing Maritime Big Data projects and initiatives funded by the European Union: datAcron, MARISA, Ranger, EUCISE, AtlantOS, EMODnet. The workshop's results enable to draw some preliminary conclusions on the current research and developments in Maritime Big Data. There is a general interest towards concrete societal and operational needs, coupled with an emerging tendency to develop methods combining heterogeneous, potentially complementary, information streams (mainly AIS, paired with SAR, Radar, METOC, acoustic), with an increasing attention towards source quality. The approaches adopted come from different areas of research, mainly machine learning and data mining, incorporating also techniques developed in Information and data fusion, but also data warehouse and online analytical processing. The current trend towards experimenting open source Big Data technologies is challenged by the integration of diversified sources of information, which comes with an increased exigence of enhanced data management capabilities for harmonised data sharing and processing that can overcome the sole exploitation of kinematic data. Meanwhile, there is a prevailing requirement to reduce the uncertainty of detection and prediction results, entailing the development of capabilities to formally handle information and source quality. Analogously, the emergence of novel Artificial Intelligence approaches that, despite showing promising results, challenge results' interpretation, requires an increased involvement of experts in all the phases of the development (the so-called ``Human in the loop"), and the holistic incorporation of approaches addressing human factors' aspects.