Trust Management as a Service: Enabling Trusted Execution in the Face of Byzantine Stakeholders
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Abstract (arXiv)
Trust is arguably the most important challenge for critical services both deployed as well as accessed remotely over the network. These systems are exposed to a wide diversity of threats, ranging from bugs to exploits, active attacks, rogue operators, or simply careless administrators. To protect such applications, one needs to guarantee that they are properly configured and securely provisioned with the "secrets" (e.g., encryption keys) necessary to preserve not only the confidentiality, integrity and freshness of their data but also their code. Furthermore, these secrets should not be kept under the control of a single stakeholder - which might be compromised and would represent a single point of failure - and they must be protected across software versions in the sense that attackers cannot get access to them via malicious updates. Traditional approaches for solving these challenges often use ad hoc techniques and ultimately rely on a hardware security module (HSM) as root of trust. We propose a more powerful and generic approach to trust management that instead relies on trusted execution environments (TEEs) and a set of stakeholders as root of trust. Our system, PALAEMON, can operate as a managed service deployed in an untrusted environment, i.e., one can delegate its operations to an untrusted cloud provider with the guarantee that data will remain confidential despite not trusting any individual human (even with root access) nor system software. PALAEMON addresses in a secure, efficient and cost-effective way five main challenges faced when developing trusted networked applications and services. Our evaluation on a range of benchmarks and real applications shows that PALAEMON performs efficiently and can protect secrets of services without any change to their source code.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2020.
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Citing papers per year: 2021: 4; 2022: 1.
5 papers.
- 2021Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGXcited by 14
- 2021secureTF: A Secure TensorFlow Frameworkcited by 12
- 2022TEE-based decentralized recommender systems: The raw data sharing redemptioncited by 6
- 2021SecFL: Confidential Federated Learning using TEEscited by 3
- 2021Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Supportcited by 2
- 2022TEE-based decentralized recommender systems: The raw data sharing redemption23 Feb 2022
- 2021SecFL: Confidential Federated Learning using TEEs3 Oct 2021
- 2021Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX4 May 2021
- 2021Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support31 Mar 2021
- 2021secureTF: A Secure TensorFlow Framework20 Jan 2021