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Because FPGAs outperform traditional processing cores like CPUs and GPUs in terms of performance per watt and flexibility, they are being used more and more in cloud and data center applications. There are growing worries about the security risks posed by multi-tenant sharing as the demand for hardware acceleration increases and gradually gives way to FPGA multi-tenancy in the cloud. The confidentiality, integrity, and availability of FPGA-accelerated applications may be compromised if space-shared FPGAs are made available to many cloud tenants. We propose a root of trust-based trusted execution mechanism called TrustToken to prevent harmful software-level attackers from getting unauthorized access and jeopardizing security. With safe key creation and truly random sources, TrustToken creates a security block that serves as the foundation of trust-based IP security. By offering crucial security characteristics, such as secure, isolated execution and trusted user interaction, TrustToken only permits trustworthy connection between the non-trusted third-party IP and the rest of the SoC environment. The suggested approach does this by connecting the third-party IP interface to the TrustToken Controller and running run-time checks on the correctness of the IP authorization(Token) signals. With an emphasis on software-based assaults targeting unauthorized access and information leakage, we offer a noble hardware/software architecture for trusted execution in FPGA-accelerated clouds and data centers.
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This content will become publicly available on October 22, 2026
Burning Fetch Execution: A Framework for Zero-Trust Multi-party Confidential Computing
How can one tamper with data that does not exist? Motivated by this question, we present the Burning Fetch eXecution (BFX) paradigm. Data in-use is vulnerable, and the current focus on encrypting and/or isolating in-use data has fallen short. Frequently reported breaches of “secure” hardware and indispensable overhead with encryption schemes confirm that trust is the modern bottleneck. This work tackles the gap in existing safeguarding technology by avoiding byte-level decryption until it is immediately fetched by the processor, only to burn it right after. We perform on-the-fetch data decryption, immediately followed by burning, i.e., erasing right after processing cycles. Thus, BFX minimizes the existence of sensitive data in-use. BFX does not demand new processing hardware units nor requires restructuring application software. Three pillars set the BFX paradigm apart: (1) zero-trust multi-party confidentiality with (2) security rooted in transparency, and (3) high performance. By tackling the root of the issue, BFX enables a zero-trust multi-partied sharing without showing scenarios that were previously unthinkable. We showcase the impact of the BFX in a scenario with a highly privileged cloud insider attacker present. We exercise a sensitive mission whereby a third-party cloud processes fourth-party confidential real-time data streamed by a drone swarm. To further highlight the zero-trust nature of BFX, we assume the inference model (code) stream-processing on swarm data to be top-secret and owned by yet another party. The unknown threat, however, is the compromised processing system (cloud) where sensitive code and data are about to be deployed by all other parties-thanks to misplaced trust.
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- Award ID(s):
- 2238476
- PAR ID:
- 10684884
- Publisher / Repository:
- Springer Nature Singapore
- Date Published:
- ISBN:
- 978-981-95-1049-8
- Page Range / eLocation ID:
- 196 to 205
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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