Accessible privacy-preserving web-based data analysis for assessing and addressing economic inequalities

An essential component of initiatives that aim to address pervasive inequalities of any kind is the ability to collect empirical evidence of both the status quo baseline and of any improvement that can be attributed to prescribed and deployed interventions. Unfortunately, two substantial barriers can arise preventing the collection and analysis of such empirical evidence: (1) the sensitive nature of the data itself and (2) a lack of technical sophistication and infrastructure available to both an initiative's beneficiaries and to those spearheading it. In the last few years, it has been shown that a cryptographic primitive called secure multi-party computation (MPC) can provide a natural technological resolution to this conundrum. MPC allows an otherwise disinterested third party to contribute its technical expertise and resources, to avoid incurring any additional liabilities itself, and (counterintuitively) to reduce the level of data exposure that existing parties must accept to achieve their data analysis goals. However, achieving these benefits requires the deliberate design of MPC tools and frameworks whose level of accessibility to non-technical users with limited infrastructure and expertise is state-of-the-art. We describe our own experiences designing, implementing, and deploying such usable web applications for secure data analysis within the context of two real-world initiatives that focus on promoting economic equality.Published versio

Challenges and paradoxes in decolonising HCI: A critical discussion

The preponderance of Western methods, practices, standards, and classifications in the manner in which new technology-related knowledge is created and globalised has led to calls for more inclusive approaches to design. A decolonisation project is concerned with how researchers might contribute to dismantling and re-envisioning existing power relations, resisting past biases, and balancing Western heavy influences in technology design by foregrounding the authentic voices of the indigenous people in the entire design process. We examine how the establishment of local Global South HCI communities (AfriCHI and ArabHCI) has led to the enactment of decolonisation practices. Specifically, we seek to uncover how decolonisation is perceived in the AfriCHI and ArabHCI communities as well as the extent to which both communities are engaged with the idea of decolonisation without necessarily using the term. We drew from the relevant literature, our own outsider/insider lived experiences, and the communities’ responses to an online anonymised survey to highlight three problematic but interrelated practical paradoxes: a terminology, an ethical, and a micro-colonisation paradox. We argue that these paradoxes expose the dilemmas faced by local non-Western researchers as they pursue decolonisation thinking. This article offers a blended perspective on the decolonisation debate in HCI, CSCW, and the practice-based CSCW scholarly communities and invites researchers to examine their research work using a decolonisation lens

Multi-regulation computing: examining the legal and policy questions that arise from secure multiparty computation

This work examines privacy laws and regulations that limit disclosure of personal data, and explores whether and how these restrictions apply when participants use cryptographically secure multi-party computation (MPC). By protecting data during use, MPC can help to foster the positive effects of data usage while mitigating potential negative impacts of data sharing in scenarios where participants want to analyze data that is subject to one or more privacy laws, especially when these laws are in apparent conflict so data cannot be shared in the clear. But paradoxically, most adoptions of MPC to date involve data that is not subject to any formal privacy regulation. We posit that a major impediment to the adoption of MPC is the difficulty of mapping this new technology onto the design principles of data privacy laws. To address this issue and with the goal of spurring adoption of MPC, this work introduces the first systematic framework to reason about the extent to which secure multiparty computation implicates data privacy laws. Our framework revolves around three questions: a definitional question on whether the encodings still constitute ‘personal data,’ a process question about whether the act of executing MPC constitutes a data disclosure event, and a liability question about what happens if something goes wrong. We conclude by providing advice to regulators and suggestions to early adoptors to spur uptake of MPC.NSF 18-209 - National Science Foundation; CNS-1915763 - National Science Foundation; HR00112020021 - Department of Defense/DARPA; CNS-1801564 - National Science Foundation; CNS-1931714 - National Science Foundation; CNS-1718135 - National Science Foundationhttps://aloni.net/wp-content/uploads/2022/08/Multi-Regulation-Computing-Walsh-Varia-Cohen-Sellars-Bestavros-ACM-CSLAW-22.pdfAccepted manuscrip

The AI gambit — leveraging artificial intelligence to combat climate change: opportunities, challenges, and recommendations

In this article we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change and it contribute to combating the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combating climate change, while reducing its impact on the environment

Secure Multi-Party Computation In Practice

Secure multi-party computation (MPC) is a cryptographic primitive for computing on private data. MPC provides strong privacy guarantees, but practical adoption requires high-quality application design, software development, and resource management. This dissertation aims to identify and reduce barriers to practical deployment of MPC applications. First, the dissertation evaluates the design, capabilities, and usability of eleven state-of-the-art MPC software frameworks. These frameworks are essential for prototyping MPC applications, but their qualities vary widely; the survey provides insight into their current abilities and limitations. A comprehensive online repository augments the survey, including complete build environments, sample programs, and additional documentation for each framework. Second, the dissertation applies these lessons in two practical applications of MPC. The first addresses algorithms for assessing stability in financial networks, traditionally designed in a full-information model with a central regulator or data aggregator. This case study describes principles to transform two such algorithms into data-oblivious versions and benchmark their execution under MPC using three frameworks. The second aims to enable unlinkability of payments made with blockchain-based cryptocurrencies. This study uses MPC in conjunction with other privacy techniques to achieve unlinkability in payment channels. Together, these studies illuminate the limitations of existing software, develop guidelines for transforming non-private algorithms into versions suitable for execution under MPC, and illustrate the current practical feasibility of MPC as a solution to a wide variety of applications

Aplicações de modelos de deep learning para monitoramento ambiental e agrícola no Brasil

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022.Algoritmos do novo campo de aprendizado de máquina conhecido como Deep Learning têm se popularizado recentemente, mostrando resultados superiores a modelos tradicionais em métodos de classificação e regressão. O histórico de sua utilização no campo do sensoriamento remoto ainda é breve, porém eles têm mostrado resultados similarmente superiores em processos como a classificação de uso e cobertura da terra e detecção de mudança. Esta tese teve como objetivo o desenvolvimento de metodologias utilizando estes algoritmos com um enfoque no monitoramento de alvos críticos no Brasil por via de imagens de satélite a fim de buscar modelos de alta precisão e acurácia para substituir metodologias utilizadas atualmente. Ao longo de seu desenvolvimento, foram produzidos três artigos onde foi avaliado o uso destes algoritmos para a detecção de três alvos distintos: (a) áreas queimadas no Cerrado brasileiro, (b) áreas desmatadas na região da Amazônia e (c) plantios de arroz no sul do Brasil. Apesar do objetivo similar na produção dos artigos, procurou-se distinguir suficientemente suas metodologias a fim de expandir o espaço metodológico conhecido para fornecer uma base teórica para facilitar e incentivar a adoção destes algoritmos em contexto nacional. O primeiro artigo avaliou diferentes dimensões de amostras para a classificação de áreas queimadas em imagens Landsat-8. O segundo artigo avaliou a utilização de séries temporais binárias de imagens Landsat para a detecção de novas áreas desmatadas entre os anos de 2017, 2018 e 2019. O último artigo utilizou imagens de radar Sentinel-1 (SAR) em uma série temporal contínua para a delimitação dos plantios de arroz no Rio Grande do Sul. Modelos similares foram utilizados em todos os artigos, porém certos modelos foram exclusivos a cada publicação, produzindo diferentes resultados. De maneira geral, os resultados encontrados mostram que algoritmos de Deep Learning são não só viáveis para detecção destes alvos mas também oferecem desempenho superior a métodos existentes na literatura, representando uma alternativa altamente eficiente para classificação e detecção de mudança dos alvos avaliados.Algorithms belonging to the new field of machine learning called Deep Learning have been gaining popularity recently, showing superior results when compared to traditional classification and regression methods. The history of their use in the field of remote sensing is not long, however they have been showing similarly superior results in processes such as land use classification and change detection. This thesis had as its objective the development of methodologies using these algorithms with a focus on monitoring critical targets in Brazil through satellite imagery in order to find high accuracy and precision models to substitute methods used currently. Through the development of this thesis, articles were produced evaluating their use for the detection of three distinct targets: (a) burnt areas in the Brazilian Cerrado, (b) deforested areas in the Amazon region and (c) rice fields in the south of Brazil. Despite the similar objective in the production of these articles, the methodologies in each of them was made sufficiently distinct in order to expand the methodological space known. The first article evaluated the use of differently sized samples to classify burnt areas in Landsat-8 imagery. The second article evaluated the use of binary Landsat time series to detect new deforested areas between the years of 2017, 2018 and 2019. The last article used continuous radar Sentinel-1 (SAR) time series to map rice fields in the state of Rio Grande do Sul. Similar models were used in all articles, however certain models were exclusive to each one. In general, the results show that not only are the Deep Learning models viable but also offer better results in comparison to other existing methods, representing an efficient alternative when it comes to the classification and change detection of the targets evaluated

Introduction to Development Engineering

This open access textbook introduces the emerging field of Development Engineering and its constituent theories, methods, and applications. It is both a teaching text for students and a resource for researchers and practitioners engaged in the design and scaling of technologies for low-resource communities. The scope is broad, ranging from the development of mobile applications for low-literacy users to hardware and software solutions for providing electricity and water in remote settings. It is also highly interdisciplinary, drawing on methods and theory from the social sciences as well as engineering and the natural sciences. The opening section reviews the history of “technology-for-development” research, and presents a framework that formalizes this body of work and begins its transformation into an academic discipline. It identifies common challenges in development and explains the book’s iterative approach of “innovation, implementation, evaluation, adaptation.” Each of the next six thematic sections focuses on a different sector: energy and environment; market performance; education and labor; water, sanitation and health; digital governance; and connectivity. These thematic sections contain case studies from landmark research that directly integrates engineering innovation with technically rigorous methods from the social sciences. Each case study describes the design, evaluation, and/or scaling of a technology in the field and follows a single form, with common elements and discussion questions, to create continuity and pedagogical consistency. Together, they highlight successful solutions to development challenges, while also analyzing the rarely discussed failures. The book concludes by reiterating the core principles of development engineering illustrated in the case studies, highlighting common challenges that engineers and scientists will face in designing technology interventions that sustainably accelerate economic development. Development Engineering provides, for the first time, a coherent intellectual framework for attacking the challenges of poverty and global climate change through the design of better technologies. It offers the rigorous discipline needed to channel the energy of a new generation of scientists and engineers toward advancing social justice and improved living conditions in low-resource communities around the world