Leonard Schild
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Leonard Schild has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Aysajan Abidin
COSIC, KU Leuven, Leuven, Belgium
3 shared publications - Bruno Volckaert
Department of Information Technology, IDLab, Ghent University - imec, Ghent, Belgium
3 shared publications - Erik Pohle
COSIC, KU Leuven, Leuven, Belgium
3 shared publications - Filip De Turck
IDLab, Ghent University - Imec, Ghent, Belgium
3 shared publications - Martin Zbudila
Department of Electrical Engineering, COSIC, KU Leuven, Leuven-Heverlee, Belgium
3 shared publications - Michiel Van Kenhove
Ghent University - imec,IDLab,Department of Information Technology,Ghent,Belgium
3 shared publications
Latest Publications
- 18 February 2026
- Published by Springer Nature in Journal of Network and Systems Management
- Vol. 34 (2) , 53
MOZAIK: A Privacy-Preserving Analytics Platform for IoT Data Using MPC and FHE
- 18 February 2026
- Published by Springer Nature in Journal of Network and Systems Management
- Vol. 34 (2) , 53
MOZAIK: A Privacy-Preserving Analytics Platform for IoT Data Using MPC and FHE
- Open Access
MOZAIK: A Privacy-Preserving Analytics Platform for IoT Data Using MPC and FHE
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.
Open AccessMOZAIK: A Privacy-Preserving Analytics Platform for IoT Data Using MPC and FHE
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.
- Open Access
- 13 October 2025
- Published by Association for Computing Machinery (ACM)
- p. 13-25
FDFB2: Full-Domain Functional Bootstrapping with Function Amortization
- 13 October 2025
- Published by Association for Computing Machinery (ACM)
- p. 13-25
Open AccessFDFB2: Full-Domain Functional Bootstrapping with Function Amortization
- 10 August 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
End-to-End Network Slicing: Securing Sensitive Data Across the Network
Internet of Things (IoT) devices are increasingly being deployed in critical applications, such as eHealth systems, enabled by advancements in 5G technology, which offer more than 100 Mbps of throughput, less than 5 ms of latency, and $99,999 \%$ of reliability. However, to overcome computing limitations and security measures, IoT devices rely on cloudbased solutions to outsource data processing. This dependency introduces significant security concerns, as sensitive data must be transmitted over the network and processed in external environments, increasing the risk of interception, unauthorized access, and data breaches. To mitigate these security risks, within the scope of the MOZAIK project, we deploy Network Slicing to ensure end-to-end inter-slice and intra-slice isolation across all network domains i.e., 5G Core (5GC), Transport Network (TN), and Radio Access Network (RAN). We deploy a synergy across the entire network infrastructure i.e., $5 \mathrm{GC}, \mathrm{TN}$, and RAN, to isolate the IoT data flows from the moment the data is generated until it reaches the cloud, safeguarding sensitive data during transmission. The results of our real-life experiments demonstrate that our proof of concept provides robust isolation between slices, effectively addressing the security concerns of IoT devices and enhancing the reliability and security of IoT applications. Additionally, we also include aspects of secure data storage and secure data processing, covered in the MOZAIK project.- 10 August 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
End-to-End Network Slicing: Securing Sensitive Data Across the Network
Internet of Things (IoT) devices are increasingly being deployed in critical applications, such as eHealth systems, enabled by advancements in 5G technology, which offer more than 100 Mbps of throughput, less than 5 ms of latency, and $99,999 \%$ of reliability. However, to overcome computing limitations and security measures, IoT devices rely on cloudbased solutions to outsource data processing. This dependency introduces significant security concerns, as sensitive data must be transmitted over the network and processed in external environments, increasing the risk of interception, unauthorized access, and data breaches. To mitigate these security risks, within the scope of the MOZAIK project, we deploy Network Slicing to ensure end-to-end inter-slice and intra-slice isolation across all network domains i.e., 5G Core (5GC), Transport Network (TN), and Radio Access Network (RAN). We deploy a synergy across the entire network infrastructure i.e., $5 \mathrm{GC}, \mathrm{TN}$, and RAN, to isolate the IoT data flows from the moment the data is generated until it reaches the cloud, safeguarding sensitive data during transmission. The results of our real-life experiments demonstrate that our proof of concept provides robust isolation between slices, effectively addressing the security concerns of IoT devices and enhancing the reliability and security of IoT applications. Additionally, we also include aspects of secure data storage and secure data processing, covered in the MOZAIK project.