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作者： Idan, Lihi Yale University

学位级别：Ph.D., Doctor of Philosophy

As the amount, complexity, and value of data available in both private and public sectors has risen sharply, the competing goals of data privacy and data utility have challenged both organizations and individuals. This dissertation addresses both goals. First, we consider the task of {\it interorganizational data sharing}, in which data owners, data clients, and data subjects have different and sometimes competing privacy concerns. A key challenge in this type of scenario is that each organization uses its own set of proprietary, intraorganizational attributes to describe the shared data; such attributes cannot be shared with other organizations. Moreover, data-access policies are determined by multiple parties and may be specified using attributes that are not directly comparablewith the ones used by the owner to specify the data. We propose a system architecture and a suite of protocols that facilitate dynamic and efficient interorganizational data sharing, while allowing each party to use its own set of proprietary attributes to describe the shared data and preserving confidentiality of both data records and *** introduce the novel technique of \textit{attribute-based encryption with oblivious attribute translation (OTABE)}, which plays a crucial role in our solution and may prove useful in other applications. This extension of attribute-based encryption uses semi-trusted proxies to enable dynamic and oblivious translation between proprietary attributes that belong to different organizations. We prove that our OTABE-based framework is secure in the standard model and provide two real-world use cases. Next, we turn our attention to utility that can be derived from the vast and growing amountof data about individuals that is available on social media. As social networks (SNs) continue to grow in popularity, it is essential to understand what can be learned about personal attributes of SN users by mining SN data. The first SN-mining problem we consider is

关键词： data exploitation Privacy protection data sharing

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A hybrid framework for industrial data storage and exploitation

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Procedia CIRP 2019年 81卷 892-897页

作者： K. Grevenitis F. Psarommatis A. Reina W. Xu I. Tourkogiorgis J. Milenkovic J. Cassina D. Kiritsis ATLANTIS Engineering S.A. 21 Ant. Tritsi str. Thessaloniki 57001 Greece École polytechnique fédérale de Lausanne ICT for Sustainable Manufacturing EPFL SCI-STI-DK Lausanne Switzerland Holonix srl 8 Corso Italia str. Meda 20821 Italy

In this paper a hybrid framework is illustrated, with a software and hardware integration strategy, for an industrial platform that exploits features from a Relational database (RDB) and Triplestore using the blackboard architectural pattern, ensuring efficient and accurate communication concerning data transfer among software applications and devices. Specifically, “Raw data Handler”, manages unstructured data from IoT devices that are kept in an Apache Cassandra instance, while “Production data Handler” acts on structured data, persisted in a MySQL database. Filtered data is transformed into knowledge and persisted into the Triplestore database (DB) and can be retrieved by expert systems at any time. The proposed framework will be tested and validated within Z-Fact0r project.

关键词： industry 4.0 IoT integration strategy data exploitation storage Triplestore production sensorial data zero-defect manufacturing

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exploitation of MaaS data for City Planning 7

Exploitation of MaaS Data for City Planning

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IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)

作者： Hatcha, Taha HafenCity Univ City Planning Hamburg Germany

ISBN: (纸本)9781665460071

The concept of Mobility as a Service is still a fresh concept, yet growing fast and have great potential to exploit, nevertheless, it requires a good understanding and a clear perception of its ecosystem, business models and data to define the right strategies of development. The research is about MaaS data exploitation for city planning, we are trying to uncover more on the exported MaaS data features and their compatibility with the good practices of city planning. To assess and measure the appropriability of this idea, Natural Language Processing algorithms were used to extract keywords related to "data" from the websites and the official documents of MaaS corporations and matched them with the relevant smart city and SDGs domains using machine learning. Measuring the compatibility and the possibilities of exploitation, is a great step towards discovering and introducing more data sources and opportunities to enhance the city planning practices and improve the decision-making system with data-integrated solutions. It also enlarges the vision of the researchers, mobility and city planning experts to develop and explore more in the urban mobility data.

关键词： MaaS data exploitation NLP SDG Smart City Machine Learning

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Artificial intelligence, digital colonialism, and the implications for Africa's future development

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data & POLICY 2024年 6卷 e67-e67页

作者： Salami, Aishat Oyenike Technol Consulting & Res Veeta Advisory Hub Lagos Nigeria

In the mid to late 19th century, much of Africa was under colonial rule, with the colonisers exercising power over the labour and territory of Africa. However, as much as Africa has predominantly gained independence from traditional colonial rule, another form of colonial rule still dominates the African landscape. This similitude of these different forms of colonialism is found in the power dominance exhibited by Western technological corporations, just like the traditional colonialists. In this digital age, digital colonialism manifests in Africa through the control and ownership of critical digital infrastructure by foreign entities, leading to unequal data flow and asymmetrical power dynamics. This usually occurs under the guise of foreign corporations providing technological assistance to the continent. By drawing references from the African continent, this article examines the manifestations of digital colonialism and the factors that aid its occurrence on the continent. It further explores the manifestations of digital colonialism in technologies such as Artificial Intelligence (AI) while analysing the occurrence of data exploitation on the continent and the need for African ownership in cultivating the digital future of the African continent. The paper also recognises the benefits linked to the use of AI and makes a cautious approach toward the deployment of AI tools in Africa. It then concludes by recommending the implementation of laws, regulations, and policies that guarantee the inclusiveness, transparency, and ethical values of new technologies, with strategies toward achieving a decolonised digital future on the African continent.

关键词： Africa artificial intelligence data exploitation digital colonialism technology dependency

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Privacy regulation in asymmetric environments

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JOURNAL OF REGULATORY ECONOMICS 2024年第2-3期66卷 157-173页

作者： Liu, Shuaicheng Shanghai Univ Finance & Econ Coll Business 777 Guoding Rd Shanghai Peoples R China

Around the world, strict privacy regulations are gradually being implemented, with the intended purpose of facilitating consumers to protect their privacy. This paper analyzes the unintended consequences of privacy regulations in the context of asymmetric data advantage. To this end, this paper constructs a model of behavior-based price discrimination, where one firm (such as the incumbent) possesses more data than the other (such as the entrant). The results demonstrate that stricter privacy regulation always benefits the data-advantaged firm. However, it has negative implications for both the data-disadvantaged firm and consumers in most cases. Furthermore, strict regulation leads to weakened competition and intensified mismatching. Therefore, this paper suggests a lenient regulatory policy.

关键词： Privacy regulation data asymmetry data acquisition data exploitation D43 L13

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Analysing disease trajectories in a cohort of 71,849 Patients: A visual analytics and statistical approach

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INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2024年 188卷 105466-105466页

作者： Kerexeta-Sarriegi, Jon Garcia-Navarro, Teresa Rollan-Martinez-Herrera, Maria Larburu, Nekane Espejo-Mambie, Moises D. Iraola, Andoni Beristain Grana, Manuel Vicomtech Fdn Basque Res & Technol Alliance BRTA Mikeletegi 57 Donostia San Sebastidn 20009 Spain eHlth Grp Biodonostia Hlth Res Inst Bioengn Area San Sebastian 20014 Spain Univ Basque Country UPV EHU Comp Sci Fac Computat IOlligence Grp San Sebastian 20018 Spain Hosp Univ Marques de Valdecilla Secc Neuropediat Serv Pediat E-39011 Santander Cantabria Spain Inst Invest Sanit IDIVAL Grp Epidemiol & Salud Publ Santander 39008 Cantabria Spain Univ Alcala Fac Med & Ciencias Salud Dept Biol Sistemas Alcala De Henares Spain Asuncion Klin Gipuzkoa 20400 Spain

Background: Disease trajectories have become increasingly relevant within the context of an aging population and the rising prevalence of chronic illnesses. Understanding the temporal progression of diseases is crucial for enhancing patient care, preventive measures, and effective management. Objective: The objective of this study is to propose and validate a novel methodology for trajectory impact analysis and interactive visualization of disease trajectories over a cohort of 71,849 patients. Methods: This article introduces an innovative comprehensive approach for analysis and interactive visualization of disease trajectories. First, Risk Increase (RI) index is defined that assesses the impact of the initial disease diagnosis on the development of subsequent illnesses. Secondly, visual graphics methods are used to represent cohort trajectories, ensuring a clear and semantically rich presentation that facilitates easy data interpretation. Results: The proposed approach is demonstrated over the disease trajectories of a cohort comprising 71,849 patients from Tolosaldea, Spain. The study finds several clinically relevant trajectories in this cohort, such as that after suffering a cerebral ischemic stroke, the probability of suffering dementia increases 10.77 times. The clinical relevance of the study outcomes have been assessed by an in-depth analysis conducted by expert clinicians. The identified disease trajectories are in agreement with the latest advancements in the field. Conclusion: The proposed approach for trajectory impact analysis and interactive visualization offers valuable graphs for the comprehensive study of disease trajectories for improved clinical decision-making. The simplicity and interpretability of our methods make them valuable approach for healthcare professionals.

关键词： Disease trajectories data mining data exploitation Graphical analysis Interactive visualization

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INTEGRATING PRE-PROCESSING PIPELINES IN ODC BASED FRAMEWORK

INTEGRATING PRE-PROCESSING PIPELINES IN ODC BASED FRAMEWORK

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Otamendi, U. Azpiroz, I. Quartulli, M. Olaizola, I. BRTA Vicomtech Fdn Donostia San Sebastian 20009 Spain

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Using on-demand processing pipelines to generate virtual geospatial products is beneficial to optimizing resource management and decreasing processing requirements and data storage space. Additionally, pre-processed products improve data quality for data-driven analytical algorithms, such as machine learning or deep learning models. This paper proposes a method to integrate virtual products based on integrating open-source processing pipelines. In order to validate and evaluate the functioning of this approach, we have integrated it into a geo-imagery management framework based on Open data Cube (ODC). To validate the methodology, we have performed three experiments developing on-demand processing pipelines using multi-sensor remote sensing data, for instance, Sentinel-1 and Sentinel-2. These pipelines are integrated using open-source processing frameworks.

关键词： SNAP Sentinel data exploitation management optimization machine learning data processing

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GEO-IMAGERY MANAGEMENT AND STATISTICAL PROCESSING IN A REGIONAL CONTEXT USING OPEN data CUBE

GEO-IMAGERY MANAGEMENT AND STATISTICAL PROCESSING IN A REGIO...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Otamendi, U. Azpiroz, L. Quariulli, M. Olaizola, I Perez, F. J. Alda, D. Gariiano, X. Vicomtech Fdn Basque Res & Technol Alliance BRTA Donostia San Sebastian 20009 Spain HAZI Fdn Granja Modelo 01192 Arkaute Spain

ISBN: (纸本)9781665403696

We propose a methodology to manage and process remote sensing and geo-imagery data for non-expert users. The proposed system provides automated data ingestion and manipulation capability for analytical data-driven purposes. In this paper, we describe the technological basis of the proposed method in addition to describing the tool architecture, the inherent data flow, and its operation in a specific use case to provide statistical summaries of Sentinel-2 regions of interest corresponding to the cultivation polygonal areas located in the Basque Country (ES).

关键词： Open data Cube Sentinel data exploitation management storage

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The Dark Side of Interaction Design

The Dark Side of Interaction Design

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CHI Conference on Human Factors in Computing Systems

作者： Rogers, Yvonne Dourish, Paul Olivier, Patrick Brereton, Margot Forlizzi, Jodi UCL UCLIC London WC1E 6BT England QUT Brisbane Qld Australia UCI Irvine CA USA CMU Pittsburgh PA USA Monash Univ Clayton Vic 3145 Australia

ISBN: (纸本)9781450380959

This panel will provoke the audience into reflecting on the dark side of interaction design. It will ask what role the HCI community has played in the inception and rise of digital addiction, digital persuasion, data exploitation and dark patterns and what to do about this state of affairs. The panelists will present their views about what we have unleashed. They will examine how 'stickiness' came about and how we might give users control over their data that is sucked up in this process. Finally, they will be asked to consider the merits and prospects of an alternative agenda, that pushes for interaction design to be fairer, more ethically-grounded and more transparent, while at the same time addressing head-on the dark side of interaction design.

关键词： Interaction Design dark side digital addiction dark patterns nudging data exploitation

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Understanding privacy concerns in ChatGPT: A data-driven approach with LDA topic modeling

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Heliyon 2024年第20期10卷 e39087页

作者： Alkamli, Shahad Alabduljabbar, Reham Information Technology Department College of Computer and Information Sciences King Saud University Riyadh 11362 Saudi Arabia

This study investigates privacy concerns associated with ChatGPT, a prominent generative AI model, through a data-driven approach combining Twitter data analysis and a user survey. Leveraging Latent Dirichlet Allocation (LDA) topic modeling and data categorization techniques, the research identifies key areas of concern: 1) Privacy Leakage Due to Public data exploitation, 2) Privacy Leakage Due to Personal Input exploitation, and 3) Privacy Leakage Due to Unauthorized Access. Twitter data analysis of over 500k tweets, supplemented by a survey of 67 ChatGPT users, reveals nuanced user perceptions and experiences regarding privacy risks. A Python program was used to improve a dataset of 500k tweets referencing “ChatGPT” during the data preparation stage. To get a refined collection of terms, steps included converting text to lowercase, eliminating mentions and hyperlinks, tokenizing, eliminating stopwords, and keyword matching to extract tweets about ChatGPT's privacy features. Once preprocessing was completed, there were 11k refined tweets. Results highlight significant apprehensions, particularly regarding unauthorized access, underscoring the importance of robust privacy measures in AI systems. The study contributes to understanding user concerns, informing policy decisions, and guiding future research on privacy in generative AI. These studies might improve ChatGPT and other AI systems' security and privacy. The public, corporations, researchers, lawmakers, and AI developers may all benefit from the useful information it provides in better understanding and managing privacy threats. © 2024

关键词： ChatGPT data categorization data exploitation Generative AI Latent dirichlet allocation (LDA) Personal input Privacy concerns Survey Twitter Unauthorized access

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