Privacy-preserving machine learning through secure management of data's lifecycle in distributed systems: REMINDER
Lead Research Organisation:
University of the West of England
Department Name: Faculty of Environment and Technology
Abstract
The Artificial Intelligence (AI) becomes ubiquitous and leading a technological paradigm shift.
Some of the main objectives set out in the United Nations' Sustainable Development Goals (SDGs) for 2030 will require to be addressed through the responsible use of AI techniques to transform data into real knowledge for the benefit of our society. This trend is being driven through an increasing degree of hyperconnectivity based on the integration of distributed systems into the Internet infrastructure mainly based on the deployment of Internet of Things (IoT) technologies as well as 5G/6G infrastructures. The integration of such systems will enable new data-based services in our surrounding environment, e.g., in the context of sustainable cities and communities or advanced eHealth services. To provide these services effectively and efficiently, a key aspect is the management of security and privacy throughout the data's lifecycle in a way that ensures the services are based on trustworthy information provided by legitimate systems. In this direction, this project (REMINDER) will design a decentralized and secure approach for the access and processing of data produced by distributed systems. In particular, REMINDER will design and implement an edge-based architecture for applications using Federated Learning (FL) that will be accessible to resource-constrained end nodes. Unlike most current deployments, the architecture will enable a collaborative model creation without the need to share the data itself. This architecture will consider the high degree of dynamism of decentralized and distributed systems by designing a node selection approach for the training process in the FL architecture while considering end systems' features (e.g., device status or battery level), as well as their evolution during their life cycle. Additionally, REMINDER will address some of the major security and privacy challenges associated with the use of decentralized Machine Learning (ML) approaches, such as FL. In this direction, the project will analyze the use of cryptographic techniques, such as Differential Privacy (DP) and Secure Multi- Party Computation (SMPC) for the sake of reaching the right balance between the effectiveness provided by ML techniques and the level of privacy being guaranteed. Data privacy will be considered in rest, transit, and while processing. The proposed solutions will be preventive and reactive. They will also ensure the privacy preserving aspects are being compliant with existing data protection regulations, such as the GDPR over the data lifecycle. REMINDER will also address some of the major security attacks in FL environments by designing and implementing an authentication protocol to ensure that only legitimate systems are able to take part in the collaborative creation process of ML models. Moreover, REMINDER will demonstrate the feasibility of the proposed research through two main use cases around eHealth and smart buildings.
Some of the main objectives set out in the United Nations' Sustainable Development Goals (SDGs) for 2030 will require to be addressed through the responsible use of AI techniques to transform data into real knowledge for the benefit of our society. This trend is being driven through an increasing degree of hyperconnectivity based on the integration of distributed systems into the Internet infrastructure mainly based on the deployment of Internet of Things (IoT) technologies as well as 5G/6G infrastructures. The integration of such systems will enable new data-based services in our surrounding environment, e.g., in the context of sustainable cities and communities or advanced eHealth services. To provide these services effectively and efficiently, a key aspect is the management of security and privacy throughout the data's lifecycle in a way that ensures the services are based on trustworthy information provided by legitimate systems. In this direction, this project (REMINDER) will design a decentralized and secure approach for the access and processing of data produced by distributed systems. In particular, REMINDER will design and implement an edge-based architecture for applications using Federated Learning (FL) that will be accessible to resource-constrained end nodes. Unlike most current deployments, the architecture will enable a collaborative model creation without the need to share the data itself. This architecture will consider the high degree of dynamism of decentralized and distributed systems by designing a node selection approach for the training process in the FL architecture while considering end systems' features (e.g., device status or battery level), as well as their evolution during their life cycle. Additionally, REMINDER will address some of the major security and privacy challenges associated with the use of decentralized Machine Learning (ML) approaches, such as FL. In this direction, the project will analyze the use of cryptographic techniques, such as Differential Privacy (DP) and Secure Multi- Party Computation (SMPC) for the sake of reaching the right balance between the effectiveness provided by ML techniques and the level of privacy being guaranteed. Data privacy will be considered in rest, transit, and while processing. The proposed solutions will be preventive and reactive. They will also ensure the privacy preserving aspects are being compliant with existing data protection regulations, such as the GDPR over the data lifecycle. REMINDER will also address some of the major security attacks in FL environments by designing and implementing an authentication protocol to ensure that only legitimate systems are able to take part in the collaborative creation process of ML models. Moreover, REMINDER will demonstrate the feasibility of the proposed research through two main use cases around eHealth and smart buildings.
