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Attestation is a fundamental building block to establish trust over software systems. When used in conjunction with trusted execution environments, it guarantees that genuine code is executed even when facing strong attackers, paving the…
Allowing a compromised device to receive privacy-sensitive sensor readings, or to operate a safety-critical actuator, carries significant risk. Usually, such risks are mitigated by validating the device's security state with remote…
Trusted Execution Environments (TEEs) allow the secure execution of code on remote systems without the need to trust their operators. They use static attestation as a central mechanism for establishing trust, allowing remote parties to…
SAFE is a clean-slate design for a highly secure computer system, with pervasive mechanisms for tracking and limiting information flows. At the lowest level, the SAFE hardware supports fine-grained programmable tags, with efficient and…
Homomorphic Encryption (HE) enables secure computation on encrypted data, addressing privacy concerns in cloud computing. However, the high computational cost of HE operations, particularly matrix multiplication (MM), remains a major…
Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations to be applied directly on encrypted data without requiring a secret key. This enables novel application scenarios where a client can safely…
We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE compatible neural networks with our own open-source framework and reproducible examples.…
Verifying the integrity of embedded device characteristics is required to ensure secure operation of a device. One central challenge is to securely extract and store device-specific configurations for future verification. Existing device…
Traditional approaches to vector similarity search over encrypted data rely on fully homomorphic encryption (FHE) to enable computation without decryption. However, the substantial computational overhead of FHE makes it impractical for…
Fully homomorphic encryption (FHE) enables secure computation on encrypted data, mitigating privacy concerns in cloud and edge environments. However, due to its high compute and memory demands, extensive acceleration research has been…
The increasing amount of data and the growing complexity of problems has resulted in an ever-growing reliance on cloud computing. However, many applications, most notably in healthcare, finance or defense, demand security and privacy which…
Regulations increasingly call for various assurances from machine learning (ML) model providers about their training data, training process, and model behavior. For better transparency, industry (e.g., Huggingface and Google) has adopted…
Hardware-firmware integration is becoming a productivity bottleneck due to the increasing complexity of accelerators, characterized by intricate memory hierarchies and firmware-intensive execution. While numerous verification techniques…
Modern machine learning (ML) models are expensive IP and business competitiveness often depends on keeping this IP confidential. This in turn restricts how these models are deployed; for example, it is unclear how to deploy a model…
Distributed ledgers are increasingly relied upon by industry to provide trustworthy accountability, strong integrity protection, and high availability for critical data without centralizing trust. Recently, distributed append-only logs are…
The growth of cloud computing has revolutionized data processing and storage capacities to another levels of scalability and flexibility. But in the process, it has created a huge challenge of security, especially in terms of safeguarding…
Federated learning (FL) has come forward as a critical approach for privacy-preserving machine learning in healthcare, allowing collaborative model training across decentralized medical datasets without exchanging clients' data. However,…
Interconnected embedded devices are increasingly used invarious scenarios, including industrial control, building automation, or emergency communication. As these systems commonly process sensitive information or perform safety critical…
Computational security in cryptography has a risk that computational assumptions underlying the security are broken in the future. One solution is to construct information-theoretically-secure protocols, but many cryptographic primitives…
ARM Memory Tagging Extension (MTE) is a new hardware feature introduced in ARMv8.5-A architecture, aiming to detect memory corruption vulnerabilities. The low overhead of MTE makes it an attractive solution to mitigate memory corruption…