Hiraku Morita | Searcharxiv

arXiv2026

Compliance as Code: A Study of Linux Distributions and Beyond

Compliance as code is an emerging idea about automating compliance through programmed compliance controls and checks. Given scant existing research thus far, the paper presents an empirical analysis of a compliance as code project addressing open source software (OSS) projects and products. The dataset examined covers a little over 1,500 unique compliance rules designed and implemented for 14 Linux distribution releases from five vendors. According to the results, (1) the coverage of the rules varies across the five vendors. Then, (2) the brief rationales provided for the rules do not exhibit statistical similarities but the short code snippets for these do show similarities to some extent. Furthermore, (3) as many as 24 controls are covered from over 10 different organizations, among them governmental agencies, standardization organizations, and non-profit associations. Finally, (4) the rules can be mapped to the essential cyber security requirements of the Cyber Resilience Act (CRA), although only modest agreement exists among the three authors regarding individual mappings. This observation supports an argument that the compliance as code project studied could be updated with new compliance checks. Given that also operating systems are in the CRA's scope when used in a network-connected product, such an updating would have also practical relevance in the nearby future.

cs.SE↗

arXiv2021

Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation

Privacy-preserving machine learning (PPML) aims at enabling machine learning (ML) algorithms to be used on sensitive data. We contribute to this line of research by proposing a framework that allows efficient and secure evaluation of full-fledged state-of-the-art ML algorithms via secure multi-party computation (MPC). This is in contrast to most prior works, which substitute ML algorithms with approximated "MPC-friendly" variants. A drawback of the latter approach is that fine-tuning of the combined ML and MPC algorithms is required, which might lead to less efficient algorithms or inferior quality ML. This is an issue for secure deep neural networks (DNN) training in particular, as this involves arithmetic algorithms thought to be "MPC-unfriendly", namely, integer division, exponentiation, inversion, and square root. In this work, we propose secure and efficient protocols for the above seemingly MPC-unfriendly computations. Our protocols are three-party protocols in the honest-majority setting, and we propose both passively secure and actively secure with abort variants. A notable feature of our protocols is that they simultaneously provide high accuracy and efficiency. This framework enables us to efficiently and securely compute modern ML algorithms such as Adam and the softmax function "as is", without resorting to approximations. As a result, we obtain secure DNN training that outperforms state-of-the-art three-party systems; our full training is up to 6.7 times faster than just the online phase of the recently proposed FALCON@PETS'21 on a standard benchmark network. We further perform measurements on real-world DNNs, AlexNet and VGG16. The performance of our framework is up to a factor of about 12-14 faster for AlexNet and 46-48 faster for VGG16 to achieve an accuracy of 70% and 75%, respectively, when compared to FALCON.

cs.CR↗