Blockchain For Food: Making Sense of Technology and the Impact on Biofortified Seeds

The global food system is under pressure and is in the early stages of a major transition towards more transparency, circularity, and personalisation. In the coming decades, there is an increasing need for more food production with fewer resources. Thus, increasing crop yields and nutritional value per crop is arguably an important factor in this global food transition. Biofortification can play an important role in feeding the world. Biofortified seeds create produce with increased nutritional values, mainly minerals and vitamins, while using the same or less resources as non-biofortified variants. However, a farmer cannot distinguish a biofortified seed from a regular seed. Due to the invisible nature of the enhanced seeds, counterfeit products are common, limiting wide-scale adoption of biofortified crops. Fraudulent seeds pose a major obstacle in the adoption of biofortified crops. A system that could guarantee the origin of the biofortified seeds is therefore required to ensure widespread adoption. This trust-ensuring immutable proof for the biofortified seeds, can be provided via blockchain technology

Trustworthy Edge Machine Learning: A Survey

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table

Privacy-preserving artificial intelligence in healthcare: Techniques and applications

There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions. [Abstract copyright: Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Reconfigurable Cyber-Physical System for Lifestyle Video-Monitoring via Deep Learning

Indoor monitoring of people at their homes has become a popular application in Smart Health. With the advances in Machine Learning and hardware for embedded devices, new distributed approaches for Cyber-Physical Systems (CPSs) are enabled. Also, changing environments and need for cost reduction motivate novel reconfigurable CPS architectures. In this work, we propose an indoor monitoring reconfigurable CPS that uses embedded local nodes (Nvidia Jetson TX2). We embed Deep Learning architectures to address Human Action Recognition. Local processing at these nodes let us tackle some common issues: reduction of data bandwidth usage and preservation of privacy (no raw images are transmitted). Also real-time processing is facilitated since optimized nodes compute only its local video feed. Regarding the reconfiguration, a remote platform monitors CPS qualities and a Quality and Resource Management (QRM) tool sends commands to the CPS core to trigger its reconfiguration. Our proposal is an energy-aware system that triggers reconfiguration based on energy consumption for battery-powered nodes. Reconfiguration reduces up to 22% the local nodes energy consumption extending the device operating time, preserving similar accuracy with respect to the alternative with no reconfiguration

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space

Uncovering the Complexities of Intellectual Property Management in the era of AI: Insights from a Bibliometric Analysis

Intellectual property (IP) management has posed continuous problems in the digital world, so understanding its associated concepts and the particularities they present is crucial. Within artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) have enabled the intelligent processing and analysis of large volumes of data, making them widely used tools. In order to help fill the research gap that exists due to the novelty of the concepts, a bibliometric analysis is proposed of 404 scientific documents linked to AI, ML, NLP and IP, extracted from the Web of Science (WoS) core collection repository. The results demonstrate a current trend in research on the management of IP, related to digital tools and highlight the issues that arise from the management of IP stemming from their use. This research also identifies how these tools have been used to facilitate the management and identification of IP. In this sense, this study brings originality to the field of intellectual property management by examining previous studies and proposing new avenues for future research, thus broadening the current understanding of the subject. Entrepreneurs and business leaders can benefit from this study as it uncovers the complexities of IP management and thus enhances understanding of the opportunities and challenges in the AI er

Lattice-Based Cryptography for Privacy Preserving Machine Learning

The digitization of healthcare data has presented a pressing need to address privacy concerns within the realm of machine learning for healthcare institutions. One promising solution is Federated Learning (FL), which enables collaborative training of deep machine learning models among medical institutions by sharing model parameters instead of raw data. This study focuses on enhancing an existing privacy-preserving federated learning algorithm for medical data through the utilization of homomorphic encryption, building upon prior research. In contrast to the previous paper this work is based upon by Wibawa, using a single key for homomorphic encryption, our proposed solution is a practical implementation of a preprint by Ma Jing et. al. with a proposed encryption scheme (xMK-CKKS) for implementing multi-key homomorphic encryption. For this, our work first involves modifying a simple “ring learning with error” RLWE scheme. We then fork a popular FL framework for python where we integrate our own communication process with protocol buffers before we locate and modify the library’s existing training loop in order to further enhance the security of model updates with the multi-key homomorphic encryption scheme. Our experimental evaluations validate that despite these modifications, our proposed framework maintains robust model performance, as demonstrated by consistent metrics including validation accuracy, precision, f1-score, and recall

Personalized question-based cybersecurity recommendation systems

En ces temps de pandémie Covid19, une énorme quantité de l’activité humaine est modifiée pour se faire à distance, notamment par des moyens électroniques. Cela rend plusieurs personnes et services vulnérables aux cyberattaques, d’où le besoin d’une éducation généralisée ou du moins accessible sur la cybersécurité. De nombreux efforts sont entrepris par les chercheurs, le gouvernement et les entreprises pour protéger et assurer la sécurité des individus contre les pirates et les cybercriminels. En raison du rôle important joué par les systèmes de recommandation dans la vie quotidienne de l'utilisateur, il est intéressant de voir comment nous pouvons combiner les systèmes de cybersécurité et de recommandation en tant que solutions alternatives pour aider les utilisateurs à comprendre les cyberattaques auxquelles ils peuvent être confrontés. Les systèmes de recommandation sont couramment utilisés par le commerce électronique, les réseaux sociaux et les plateformes de voyage, et ils sont basés sur des techniques de systèmes de recommandation traditionnels. Au vu des faits mentionnés ci-dessus, et le besoin de protéger les internautes, il devient important de fournir un système personnalisé, qui permet de partager les problèmes, d'interagir avec un système et de trouver des recommandations. Pour cela, ce travail propose « Cyberhelper », un système de recommandation de cybersécurité personnalisé basé sur des questions pour la sensibilisation à la cybersécurité. De plus, la plateforme proposée est équipée d'un algorithme hybride associé à trois différents algorithmes basés sur la connaissance, les utilisateurs et le contenu qui garantit une recommandation personnalisée optimale en fonction du modèle utilisateur et du contexte. Les résultats expérimentaux montrent que la précision obtenue en appliquant l'algorithme proposé est bien supérieure à la précision obtenue en utilisant d'autres mécanismes de système de recommandation traditionnels. Les résultats suggèrent également qu'en adoptant l'approche proposée, chaque utilisateur peut avoir une expérience utilisateur unique, ce qui peut l'aider à comprendre l'environnement de cybersécurité.With the proliferation of the virtual universe and the multitude of services provided by the World Wide Web, a major concern arises: Security and privacy have never been more in jeopardy. Nowadays, with the Covid 19 pandemic, the world faces a new reality that pushed the majority of the workforce to telecommute. This thereby creates new vulnerabilities for cyber attackers to exploit. It’s important now more than ever, to educate and offer guidance towards good cybersecurity hygiene. In this context, a major effort has been dedicated by researchers, governments, and businesses alike to protect people online against hackers and cybercriminals. With a focus on strengthening the weakest link in the cybersecurity chain which is the human being, educational and awareness-raising tools have been put to use. However, most researchers focus on the “one size fits all” solutions which do not focus on the intricacies of individuals. This work aims to overcome that by contributing a personalized question-based recommender system. Named “Cyberhelper”, this work benefits from an existing mature body of research on recommender system algorithms along with recent research on non-user-specific question-based recommenders. The reported proof of concept holds potential for future work in adapting Cyberhelper as an everyday assistant for different types of users and different contexts

ENNigma: A Framework for Private Neural Networks

The increasing concerns about data privacy and the stringent enforcement of data protection laws are placing growing pressure on organizations to secure large datasets. The challenge of ensuring data privacy becomes even more complex in the domains of Artificial Intelligence and Machine Learning due to their requirement for large amounts of data. While approaches like differential privacy and secure multi-party computation allow data to be used with some privacy guarantees, they often compromise data integrity or accessibility as a tradeoff. In contrast, when using encryption-based strategies, this is not the case. While basic encryption only protects data during transmission and storage, Homomorphic Encryption (HE) is able to preserve data privacy during its processing on a centralized server. Despite its advantages, the computational overhead HE introduces is notably challenging when integrated into Neural Networks (NNs), which are already computationally expensive. In this work, we present a framework called ENNigma, which is a Private Neural Network (PNN) that uses HE for data privacy preservation. Unlike some state-of-the-art approaches, ENNigma guarantees data security throughout every operation, maintaining this guarantee even if the server is compromised. The impact of this privacy preservation layer on the NN performance is minimal, with the only major drawback being its computational cost. Several optimizations were implemented to maximize the efficiency of ENNigma, leading to occasional computational time reduction above 50%. In the context of the Network Intrusion Detection System application domain, particularly within the sub-domain of Distributed Denial of Service attack detection, several models were developed and employed to assess ENNigma’s performance in a real-world scenario. These models demonstrated comparable performance to non-private NNs while also achiev ing the two-and-a-half-minute inference latency mark. This suggests that our framework is approaching a state where it can be effectively utilized in real-time applications. The key takeaway is that ENNigma represents a significant advancement in the field of PNN as it ensures data privacy with minimal impact on NN performance. While it is not yet ready for real-world deployment due to its computational complexity, this framework serves as a milestone toward realizing fully private and efficient NNs.As preocupações crescentes com a privacidade de dados e a implementação de leis que visam endereçar este problema, estão a pressionar as organizações para assegurar a segurança das suas bases de dados. Este desafio torna-se ainda mais complexo nos domínios da Inteligência Artificial e Machine Learning, que dependem do acesso a grandes volumes de dados para obterem bons resultados. As abordagens existentes, tal como Differential Privacy e Secure Multi-party Computation, já permitem o uso de dados com algumas garantias de privacidade. No entanto, na maioria das vezes, comprometem a integridade ou a acessibilidade aos mesmos. Por outro lado, ao usar estratégias baseadas em cifras, isso não ocorre. Ao contrário das cifras mais tradicionais, que apenas protegem os dados durante a transmissão e armazenamento, as cifras homomórficas são capazes de preservar a privacidade dos dados durante o seu processamento. Nomeadamente se o mesmo for centralizado num único servidor. Apesar das suas vantagens, o custo computacional introduzido por este tipo de cifras é bastante desafiador quando integrado em Redes Neurais que, por natureza, já são computacionalmente pesadas. Neste trabalho, apresentamos uma biblioteca chamada ENNigma, que é uma Rede Neural Privada construída usando cifras homomórficas para preservar a privacidade dos dados. Ao contrário de algumas abordagens estado-da-arte, a ENNigma garante a segurança dos dados em todas as operações, mantendo essa garantia mesmo que o servidor seja comprometido. O impacto da introdução desta camada de segurança, no desempenho da rede neural, é mínimo, sendo a sua única grande desvantagem o seu custo computacional. Foram ainda implementadas diversas otimizações para maximizar a eficiência da biblioteca apresentada, levando a reduções ocasionais no tempo computacional acima de 50%. No contexto do domínio de aplicação de Sistemas de Detecção de Intrusão em Redes de Computadores, em particular dentro do subdomínio de detecção de ataques do tipo Distributed Denial of Service, vários modelos foram desenvolvidos para avaliar o desempenho da ENNigma num cenário real. Estes modelos demonstraram desempenho comparável às redes neurais não privadas, ao mesmo tempo que alcançaram uma latência de inferência de dois minutos e meio. Isso sugere que a biblioteca apresentada está a aproximar-se de um estado em que pode ser utilizada em aplicações em tempo real. A principal conclusão é que a biblioteca ENNigma representa um avanço significativo na área das Redes Neurais Privadas, pois assegura a privacidade dos dados com um impacto mínimo no desempenho da rede neural. Embora esta ferramenta ainda não esteja pronta para utilização no mundo real, devido à sua complexidade computacional, serve como um marco importante para o desenvolvimento de redes neurais totalmente privadas e eficientes