Michiel Van Kenhove

The rapid increase of Internet of Things (IoT) systems across several domains has led to the generation of vast volumes of sensitive data, presenting significant challenges in terms of storage and data analytics. Cloud-assisted IoT solutions offer storage, scalability, and computational resources, but introduce new security and privacy risks that conventional trust-based approaches fail to adequately mitigate. To address these challenges, this paper presents MOZAIK, a novel end-to-end privacy-preserving confidential data storage and distributed processing architecture tailored for IoT-to-cloud scenarios. MOZAIK ensures that data remains encrypted throughout its lifecycle, including during transmission, storage, and processing. This is achieved by employing a cryptographic privacy-enhancing technology known as computing on encrypted data (COED). Two distinct COED techniques are explored, specifically secure multi-party computation (MPC) and fully homomorphic encryption (FHE). The paper includes a comprehensive analysis of the MOZAIK architecture, including a proof-of-concept implementation and performance evaluations. The evaluation results demonstrate the feasibility of the MOZAIK system and indicate the cost of an end-to-end privacy-preserving system compared to regular plaintext alternatives. All components of the MOZAIK platform are released as open-source software alongside this publication, with the aim of advancing secure and privacy-preserving data processing practices.

The rapid expansion of Internet of Things (IoT), edge, and embedded devices in the past decade has introduced numerous challenges in terms of security and configuration management. Simultaneously, advances in cloud-native development practices have greatly enhanced the development experience and facilitated quicker updates, thereby enhancing application security. However, applying these advances to IoT, edge, and embedded devices remains a complex task, primarily due to the heterogeneous environments and the need to support devices with extended lifespans. WebAssembly and the WebAssembly System Interface (WASI) has emerged as a promising technology to bridge this gap. As WebAssembly becomes more popular on IoT, edge, and embedded devices, there is a growing demand for hardware interface support in WebAssembly programs. This work presents WASI proposals and proof-of-concept implementations to enable hardware interaction with I2C and USB, which are two commonly used protocols in IoT, directly from WebAssembly applications. This is achieved by running the device drivers within WebAssembly as well. A thorough evaluation of the proof of concepts shows that WASI-USB introduces a minimal overhead of at most 8% compared to native operating system USB APIs. However, the results show that runtime initialization overhead can be significant in low-latency applications.

Anatomy learning has traditionally relied on drawings, plastic models, and cadaver dissections/prosections to help students understand the three-dimensional (3D) relationships within the human body. However, the landscape of anatomy education has been transformed with the introduction of digital media. In this light, the Open Anatomy Explorer (OPANEX) was developed. It includes two user interfaces (UI): one for students and one for administrators. The administrator UI offers features such as uploading and labelling of 3D models, and customizing 3D settings. Additionally, the OPANEX facilitates content sharing between institutes through its import-export functionality. To evaluate the integration of OPANEX within the existing array of learning resources, a survey was conducted as part of the osteology course at Ghent University, Belgium. The survey aimed to investigate the frequency of use of five learning resources, attitudes towards 3D environments, and the OPANEX user experience. Analysis revealed that the OPANEX was the most frequently used resource. Students' attitudes towards 3D learning environments further supported this preference. Feedback on the OPANEX user experience indicated various reasons for its popularity, including the quality of the models, regional annotations, and customized learning content. In conclusion, the outcomes underscore the educational value of the OPANEX, reflecting students' positive attitudes towards 3D environments in anatomy education.

The shift to the cloud among organizations has surged enormously over the last decade. As a result, an overwhelming amount of new cloud-based technologies emerged, making it increasingly more challenging to compare the different technologies and to identify the ideal technology that aligns best with a specific use case. The performance and resource usage of a system or software technology can be assessed by running benchmarks. Performing benchmarks has proven to be a time-consuming and error-prone task, especially when executing multiple consecutive tests on the same system. This sparked curiosity in exploring the feasibility of automating this manual benchmarking process. This paper proposes Cloud-NativeBench (CNB), a novel open source benchmarking framework implemented as a Kubernetes operator that fully automates the benchmarking cycle. The entire process, including benchmark deployment, the consecutive execution of benchmarks in a queue, results collection, and statistical data analysis, is fully automated. The framework is designed to be extensible without the need to adapt the operator itself, enabling users to develop fine-tuned custom benchmarks according to their specific use cases. A detailed evaluation shows the ease-of-use of Cloud-Native-Bench and how it streamlines the process of running benchmarks in cloud-native environments. Experiments show the importance of running benchmarks on cloud technologies. For example, employing a different web server technology can increase the mean throughput by 252%.

The immense growth of data from the proliferation of Internet of Things (IoT) devices presents opportunities and challenges for privacy engineering. On the one hand, this data can be harnessed for personalized services, cost savings, and environmental benefits. On the other hand, (new) legislation must be complied with and privacy risks arise from collecting and processing of such data. Distributed privacy-preserving analytics offers a promising solution, providing insights while also protecting privacy. However, this approach has new challenges and risks, such as key management and confidentiality. When designing a data marketplace which offers distributed privacy-preserving analytics, the key management comes with different threats, which require a solution adapted to the distributed architecture.In this context, the paper presents a comprehensive, end-to-end secure system called MOZAIK for privacy-preserving data collection, analysis, and sharing. The article focuses on the key management aspect of the secure multi-party computation (MPC) component in a distributed privacy-preserving analytics architecture and the specific challenges created by introducing MPC. The proposed solution involves temporary storage of (symmetric) key shares and public-key encryption schemes to ensure secure key management for privacy-preserving computation. Our solution has the potential to be applied in other MPC-based setups, making it a valuable addition to the field of privacy engineering. By addressing key management challenges and risks, MOZAIK enhances data protection while enabling valuable insights from IoT data.