Trust me, I’m an Intermediary! Exploring Data Intermediation Services

Data ecosystems receive considerable attention in academia and practice, as indicated by a steadily growing body of research and large-scale (industry-driven) research projects. They can leverage so-called data intermediaries, which are mediating parties that facilitate data sharing between a data provider and a data consumer. Research has uncovered many types of data intermediaries, such as data marketplaces or data trusts. However, what is missing is a ‘big picture’ of data intermediaries and the functions they fulfill. We tackle this issue by extracting data intermediation services decoupled from specific instances to give a comprehensive overview of how they work. To achieve this, we report on a systematic literature review, contributing data intermediation services

A Survey of Smart Energy Services for Private Households

The energy sector is challenged by the ongoing digitalization with emerging smart energy products and services. Smart energy products such as smart meters leverage innovative smart energy services promising both new business opportunities and values for customers. Smart products and services could enhance energy efficiency as well as enable private households to produce their own energy. Although services are regarded as a bridge to the customer, research on smart energy services is scarce. To address the gap, we assess smart energy services discussed in research and in the German consumer market and compare the findings from literature with the real market. Our survey provides researchers and practitioners with an overview of smart energy services and can serve as a starting point for service design, which in turn can support the diffusion of energy saving technologies

Crowds for Clouds: Recent Trends in Humanities Research Infrastructures

Humanities have convincingly argued that they need transnational research opportunities and through the digital transformation of their disciplines also have the means to proceed with it on an up to now unknown scale. The digital transformation of research and its resources means that many of the artifacts, documents, materials, etc. that interest humanities research can now be combined in new and innovative ways. Due to the digital transformations, (big) data and information have become central to the study of culture and society. Humanities research infrastructures manage, organise and distribute this kind of information and many more data objects as they becomes relevant for social and cultural research

Bad hand? The “new deal” for EU consumers

On 11 April 2018, the European Commission presented its "New Deal for Consumers". The "New Deal" addresses four key areas: (1) General improvements to Consumer Protection rules (2) An increased ability to impose sanctions on traders who fail to comply with consumer protection rules (3) The introduction of a representative action (4) Taking action to stop "dual quality" goods (i.e., goods sold under one brand etc. but of varying levels of quality in different Member States). The focus of this contribution is on (1), (2) and (4), which are all dealt with by the proposal for the Modernisation Directive

Assessing the role of big data and the Internet of things on the transition to circular economy: part II: an extension of the ReSOLVE framework proposal through a literature review

This paper presents the main findings of a literature-based study of circular economy (CE) extending the technology attributes present on the Ellen MacArthur Foundation (EMF) Regenerate, Share, Optimise, Loop, Virtualise and Exchange (ReSOLVE) framework. The introduction and methods were presented in Part I (1). Part II concludes that there are 39 capabilities grouped into six elementary CE principles and five action groups, with public administration being the most interested sector, forming the CE information technology (IT) capabilities framework. It is expected the framework can be used as a diagnostic tool to allow organisations to evaluate their technological gaps and plan their IT investments to support the transition to CE.Indisponível

Applicability of Industry 4.0 Technologies in the Reverse Logistics: A Circular Economy Approach Based on COmprehensive Distance Based RAnking (COBRA) Method

The logistics sector plays one of the most important roles in the supply chain with the aim of providing a fast, flexible, safe, economical, efficient, and environmentally acceptable performance of freight transport flows. In addition, the popularization of the concept of a circular economy (CE) used to retain goods, components, and materials at their highest usability and value at all times, illustrates the importance of the adequate performance of reverse logistics (RL) processes. However, traditional RL is unable to cope with the requirements of modern supply chains and requires the application of Industry 4.0 technologies, which would make it more efficient. The main aim of this study was to evaluate the applicability of various Industry 4.0 technologies in the RL sector in order to point out the most applicable ones. To solve the defined problem, a novel multi-criteria decision making (MCDM) model was defined by combining the best-worst method (BWM) to obtain the criteria weights, and the newly developed comprehensive distance-based ranking (COBRA) method to rank the technologies. Another aim of the study was to validate the newly established method. The results indicated that the most applicable technologies were the Internet of Things, cloud computing, and electronic-mobile marketplaces. These technologies will have a significant impact on the development of RL and the establishment of CE systems, thus bringing about all the related positive effects

Rethinking consumers\u27 data sharing decisions with the emergence of multi-party computation: an experimental design for evaluation

Consumers are increasingly reluctant to share their personal data with businesses due to mounting concerns over privacy and control. Emerging privacy-enhancing technologies like multi-party computation (MPC), which allows generating insights while consumers retain data control, are challenging the current understanding of why consumers share their data. In this research-in-progress paper, we develop and evaluate an instrument and experimental design to investigate the impact of MPC on consumers’ willingness to share data and its antecedents. Preliminary analysis from a pre-study (N=300) indicates a good fit for our model. Also, MPC enhances consumers’ control and trust while reducing privacy concerns and risk, ultimately increasing data sharing willingness. The findings suggest that privacy-enhancing technologies significantly affect both the willingness to share data itself and its typical antecedents. The next step will conduct a large-scale online experiment using the developed instruments to evaluate further the impact of MPC on consumers’ willingness to share data

Machine Learning Models that Remember Too Much

Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data. We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that "memorize" information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data

Platform Architectures: The Structuration of Platform Companies on the Internet

Today's internet is shaped largely by privately operated platforms of various kinds. This paper asks how the various commercially operated communication, market, consumption and service platforms can be grasped as a distinct organizational form of enterprise. To this end, we make a basic distinction between (1) the platform-operating companies as organizing and structuring cores whose goal is to run a profitable business, and (2) the platforms belonging to these companies as more or less extensive, rule-based and strongly technically mediated social action spaces. While platform companies are essentially organizations in an almost archetypical sense, the internet platforms they operate constitute socio-technically structured social, market, consumption or service spaces in which social actors interact on the basis of detailed and technically framed rules, albeit, at the same time, in a varied and idiosyncratic manner. The thesis of this paper is that the coordination, control and exploitation mechanisms characteristic of the platform architectures are characterized by a strong hierarchical orientation in which elements of co-optation and the orchestrated participation of users are embedded. In this hybrid constellation, the platform companies have a high degree of structure-giving, rule-setting and controlling power - in addition to exclusive access to the raw data material generated there. While this power may manifest, at times, as rigid control, direct coercion or en-forceable accountability, for the majority of rule-obeying users it unfolds nearly imperceptibly and largely silently beneath the surface of a (supposed) openness that likewise characterizes the platforms as technically mediated spaces for social and economic exchange