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Two-Way Confidential VMs (2cVM): Collaborative Confidential Computing for Mutually Distrustful Parties
Authors:
Jordi Thijsman,
Merlijn Sebrechts,
Stefan Lefever,
Filip De Turck,
Bruno Volckaert
Abstract:
Collaborative computation across organizations is often constrained by the need to process sensitive data and proprietary code without exposing them to untrusted infrastructure or participants. Cryptographic approaches such as fully homomorphic encryption and secure multi-party computation provide strong confidentiality but remain impractical for general workloads due to their extreme computationa…
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Collaborative computation across organizations is often constrained by the need to process sensitive data and proprietary code without exposing them to untrusted infrastructure or participants. Cryptographic approaches such as fully homomorphic encryption and secure multi-party computation provide strong confidentiality but remain impractical for general workloads due to their extreme computational cost. We present the Two-Way Confidential Virtual Machine (2cVM), a two-layer architecture that pairs a hardware trusted execution environment with an intra-workload isolation layer. Unlike regular Confidential Virtual Machines, 2cVM enforces mutual isolation between co-resident workloads, ensuring that participants retain control over their data and code. All computation in 2cVM is governed by a Commitment Manifest that enumerates participants, component composition, permitted data channels, and authorized outputs; the manifest is locked to the VM and incorporated into attestation evidence, making the policy immutable and independently verifiable throughout the VM's lifetime. A proof-of-concept realization combines AMD SEV-SNP for hardware protection with the WebAssembly Component Model for fine-grained sandboxing of participant code. Evaluation on commodity hardware across four benchmark classes shows that the two isolation layers do not accumulate linearly: once a workload executes inside the WebAssembly sandbox, the marginal cost of enabling hardware memory protection is small. Overhead is workload-dependent, governed primarily by memory access pattern, ranging from negligible for sequential workloads to approximately 2x for irregular, pointer-chasing access patterns. These results indicate that 2cVM provides a practical and verifiable foundation for privacy-preserving collaborative computation.
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Submitted 10 June, 2026; v1 submitted 9 June, 2026;
originally announced June 2026.
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TrustMee: Self-Verifying Remote Attestation Evidence
Authors:
Parsa Sadri Sinaki,
Zainab Ahmad,
Wentao Xie,
Merlijn Sebrechts,
Jimmy Kjällman,
Lachlan J. Gunn
Abstract:
Remote attestation allows a TEE to attest its own state to a remote party, and is used in the Confidential Computing ecosystem to provide assurance that data and code are available only to authorized hardware and software. This functionality can then be incorporated into higher-level platforms, allowing workloads to attest themselves to a remote party. However, supporting each new platform enlarge…
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Remote attestation allows a TEE to attest its own state to a remote party, and is used in the Confidential Computing ecosystem to provide assurance that data and code are available only to authorized hardware and software. This functionality can then be incorporated into higher-level platforms, allowing workloads to attest themselves to a remote party. However, supporting each new platform enlarges a Verifier's trusted computing base, since it must include code to verify the attestation evidence from each platform, often performing risky operations e.g., binary parsing. This means that emerging or niche Attester platforms cannot be supported by standard Verifier software, preventing them from interacting with the broader confidential computing ecosystem. We introduce self-verifying remote attestation evidence. Each attestation bundle identifies its verification logic in the form of a WebAssembly component that is downloaded by the Verifier and executed. This approach transforms evidence verification into a platform-agnostic functionality that is implemented once for all platforms: the Verifier executes the supplied verification logic to validate the evidence and incorporates its hash and, when available, the signer's public key as identity claims into the attestation result. As a result, Verifiers can validate attestation evidence without any platform-specific code; the verification logic is just another measurement whose reference value can be checked with existing mechanisms. We implement this concept as TrustMee, a platform-agnostic verification driver for the Trustee framework. We demonstrate self-verifying evidence for AMD SNP, Intel TDX, Intel SGX, Kata agent-policy appraisal, and ReCFA control-flow attestations, producing attestation claims in the standard EAT format. The last two are part of the software stack, and not supported by existing Verifiers such as Trustee.
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Submitted 20 August, 2026; v1 submitted 13 February, 2026;
originally announced February 2026.
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AIBoMGen: Generating an AI Bill of Materials for Secure, Transparent, and Compliant Model Training
Authors:
Wiebe Vandendriessche,
Jordi Thijsman,
Laurens D'hooge,
Bruno Volckaert,
Merlijn Sebrechts
Abstract:
The rapid adoption of complex AI systems has outpaced the development of tools to ensure their transparency, security, and regulatory compliance. In this paper, the AI Bill of Materials (AIBOM), an extension of the Software Bill of Materials (SBOM), is introduced as a standardized, verifiable record of trained AI models and their environments. Our proof-of-concept platform, AIBoMGen, automates the…
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The rapid adoption of complex AI systems has outpaced the development of tools to ensure their transparency, security, and regulatory compliance. In this paper, the AI Bill of Materials (AIBOM), an extension of the Software Bill of Materials (SBOM), is introduced as a standardized, verifiable record of trained AI models and their environments. Our proof-of-concept platform, AIBoMGen, automates the generation of signed AIBOMs by capturing datasets, model metadata, and environment details during training. The training platform acts as a neutral, third-party observer and root of trust. It enforces verifiable AIBOM creation for every job. The system uses cryptographic hashing, digital signatures, and in-toto attestations to ensure integrity and protect against threats such as artifact tampering by dishonest model creators. Our evaluation demonstrates that AIBoMGen reliably detects unauthorized modifications to all artifacts and can generate AIBOMs with negligible performance overhead. These results highlight the potential of AIBoMGen as a foundational step toward building secure and transparent AI ecosystems, enabling compliance with regulatory frameworks like the EUs AI Act.
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Submitted 9 January, 2026;
originally announced January 2026.
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MOZAIK: A Privacy-Preserving Analytics Platform for IoT Data Using MPC and FHE
Authors:
Michiel Van Kenhove,
Erik Pohle,
Leonard Schild,
Martin Zbudila,
Merlijn Sebrechts,
Filip De Turck,
Bruno Volckaert,
Aysajan Abidin
Abstract:
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 adequat…
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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.
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Submitted 5 January, 2026;
originally announced January 2026.
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Delta Sum Learning: an approach for fast and global convergence in Gossip Learning
Authors:
Tom Goethals,
Merlijn Sebrechts,
Stijn De Schrijver,
Filip De Turck,
Bruno Volckaert
Abstract:
Federated Learning is a popular approach for distributed learning due to its security and computational benefits. With the advent of powerful devices in the network edge, Gossip Learning further decentralizes Federated Learning by removing centralized integration and relying fully on peer to peer updates. However, the averaging methods generally used in both Federated and Gossip Learning are not i…
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Federated Learning is a popular approach for distributed learning due to its security and computational benefits. With the advent of powerful devices in the network edge, Gossip Learning further decentralizes Federated Learning by removing centralized integration and relying fully on peer to peer updates. However, the averaging methods generally used in both Federated and Gossip Learning are not ideal for model accuracy and global convergence. Additionally, there are few options to deploy Learning workloads in the edge as part of a larger application using a declarative approach such as Kubernetes manifests. This paper proposes Delta Sum Learning as a method to improve the basic aggregation operation in Gossip Learning, and implements it in a decentralized orchestration framework based on Open Application Model, which allows for dynamic node discovery and intent-driven deployment of multi-workload applications. Evaluation results show that Delta Sum performance is on par with alternative integration methods for 10 node topologies, but results in a 58% lower global accuracy drop when scaling to 50 nodes. Overall, it shows strong global convergence and a logarithmic loss of accuracy with increasing topology size compared to a linear loss for alternatives under limited connectivity.
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Submitted 1 December, 2025;
originally announced December 2025.
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Cyber-physical WebAssembly: Secure Hardware Interfaces and Pluggable Drivers
Authors:
Michiel Van Kenhove,
Maximilian Seidler,
Friedrich Vandenberghe,
Warre Dujardin,
Wouter Hennen,
Arne Vogel,
Merlijn Sebrechts,
Tom Goethals,
Filip De Turck,
Bruno Volckaert
Abstract:
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 t…
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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.
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Submitted 2 October, 2025; v1 submitted 30 October, 2024;
originally announced October 2024.
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Trusting the Cloud-Native Edge: Remotely Attested Kubernetes Workers
Authors:
Jordi Thijsman,
Merlijn Sebrechts,
Filip De Turck,
Bruno Volckaert
Abstract:
A Kubernetes cluster typically consists of trusted nodes, running within the confines of a physically secure datacenter. With recent advances in edge orchestration, this is no longer the case. This poses a new challenge: how can we trust a device that an attacker has physical access to? This paper presents an architecture and open-source implementation that securely enrolls edge devices as trusted…
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A Kubernetes cluster typically consists of trusted nodes, running within the confines of a physically secure datacenter. With recent advances in edge orchestration, this is no longer the case. This poses a new challenge: how can we trust a device that an attacker has physical access to? This paper presents an architecture and open-source implementation that securely enrolls edge devices as trusted Kubernetes worker nodes. By providing boot attestation rooted in a hardware Trusted Platform Module, a strong base of trust is provided. A new custom controller directs a modified version of Keylime to cross the cloud-edge gap and securely deliver unique cluster credentials required to enroll an edge worker. The controller dynamically grants and revokes these credentials based on attestation events, preventing a possibly compromised node from accessing sensitive cluster resources. We provide both a qualitative and a quantitative evaluation of the architecture. The qualitative scenarios prove its ability to attest and enroll an edge device with role-based access control (RBAC) permissions that dynamically adjust to attestation events. The quantitative evaluation reflects an average of 10.28 seconds delay incurred on the startup time of the edge node due to attestation for a total average enrollment time of 20.91 seconds. The presented architecture thus provides a strong base of trust, securing a physically exposed edge device and paving the way for a robust and resilient edge computing ecosystem.
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Submitted 16 May, 2024;
originally announced May 2024.
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Adapting Kubernetes controllers to the edge: on-demand control planes using Wasm and WASI
Authors:
Merlijn Sebrechts,
Tim Ramlot,
Sander Borny,
Tom Goethals,
Bruno Volckaert,
Filip De Turck
Abstract:
Kubernetes' high resource requirements hamper its adoption in constrained environments such as the edge and fog. Its extensible control plane is a significant contributor to this, consisting of long-lived processes called "controllers" that constantly listen for state changes and use resources even when they are not needed. This paper presents a WebAssembly-based framework for running lightweight…
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Kubernetes' high resource requirements hamper its adoption in constrained environments such as the edge and fog. Its extensible control plane is a significant contributor to this, consisting of long-lived processes called "controllers" that constantly listen for state changes and use resources even when they are not needed. This paper presents a WebAssembly-based framework for running lightweight controllers on-demand, only when they are needed. This framework extends the WebAssembly System Interface (WASI), in order to run Kubernetes controllers as lightweight Wasm modules. The framework runs these Wasm controllers in a modified version of Wasmtime, the reference WebAssembly (Wasm) runtime, that swaps idle controllers to disk and activates them when needed. A thorough evaluation shows this framework achieves a 64% memory reduction compared to traditional container-based controller frameworks.
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Submitted 2 September, 2022;
originally announced September 2022.