Dishonest Approximate Computing: A Coming Crisis for Cloud Clients
Ye Wang, Jian Dong, Ming Han, Jin Wu, Gang Qu

TL;DR
This paper highlights the risks of malicious use of Approximate Computing in cloud services, proposing two detection methods that effectively identify dishonest AC deployments to protect clients.
Contribution
It introduces the concept of DisHonest Approximate Computing (DHAC) and presents two novel, model-free detection techniques, Residual Class Check and Forward-Backward Check, for real-time DHAC detection.
Findings
RCC and FBC detect over 96%-99% of DHAC cases
Both methods operate with minimal false positives
Detection methods are suitable for real-time cloud environments
Abstract
Approximate Computing (AC) has emerged as a promising technique for achieving energy-efficient architectures and is expected to become an effective technique for reducing the electricity cost for cloud service providers (CSP). However, the potential misuse of AC has not received adequate attention, which is a coming crisis behind the blueprint of AC. Driven by the pursuit of illegal financial profits, untrusted CSPs may deploy low-cost AC devices and deceive clients by presenting AC services as promised accurate computing products, while falsely claiming AC outputs as accurate results. This misuse of AC will cause both financial loss and computing degradation to cloud clients. In this paper, we define this malicious attack as DisHonest Approximate Computing (DHAC) and analyze the technical challenges faced by clients in detecting such attacks. To address this issue, we propose two…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Cloud Computing and Resource Management · Cryptography and Data Security
