Dishonest Approximate Computing: A Coming Crisis for Cloud Clients
<|reference_start|>Dishonest Approximate Computing: A Coming Crisis for Cloud Clients: 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 golden model free detection methods: Residual Class Check (RCC) and Forward-Backward Check (FBC). RCC provides clients a low-cost approach to infer the residual class to which a legitimate accurate output should belong. By comparing the residual class of the returned result, clients can determine whether a computing service contains any AC elements. FBC detects potential DHAC by computing an invertible check branch using the intermediate values of the program. It compares the values before entering and after returning from the check branch to identify any discrepancies. Both RCC and FBC can be executed concurrently with real computing tasks, enabling real-time DHAC detection with current inputs. Our experimental results show that both RCC and FBC can detect over 96%-99% of DHAC cases without misjudging any legitimate accurate results.<|reference_end|>
@article{wang2024dishonest,
    title={Dishonest Approximate Computing: A Coming Crisis for Cloud Clients},
    author={Ye Wang, Jian Dong, Ming Han, Jin Wu, Gang Qu},
    journal={arXiv preprint arXiv:2405.15322},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15322},
    primaryClass={cs.CR cs.AR}
}