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EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

Computers, Materials & Continua 2024年第6期79卷 4021-4044页

作者： Zhenjiang Dong Xin Ge Yuehua Huang Jiankuo Dong Jiang Xu School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210023China School of Computer Nanjing University of Information Science and TechnologyNanjing210044China

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of *** delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited ***,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving *** such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private *** its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained *** to enhance efficiency have been made,but scalability remains a *** the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising ***,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)*** the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU *** experimental results demonstrate the effectiveness of *** an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/***,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade *** solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same *** summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded

关键词： Secure two-party computation embedded GPU acceleration privacy-preserving machine learning edge computing

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Overhead-free Noise-tolerant Federated learning: A New Baseline

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machine Intelligence Research 2024年第3期21卷 526-537页

作者： Shiyi Lin Deming Zhai Feilong Zhang Junjun Jiang Xianming Liu Xiangyang Ji Department of Computer Science and Technology Harbin Institute of TechnologyHarbin150000China Department of Automation Tsinghua UniversityBeijing100084China

Federated learning (FL) is a promising decentralized machine learning approach that enables multiple distributed clients to train a model jointly while keeping their data private. However, in real-world scenarios, the supervised training data stored in local clients inevitably suffer from imperfect annotations, resulting in subjective, inconsistent and biased labels. These noisy labels can harm the collaborative aggregation process of FL by inducing inconsistent decision boundaries. Unfortunately, few attempts have been made towards noise-tolerant federated learning, with most of them relying on the strategy of transmitting overhead messages to assist noisy labels detection and correction, which increases the communication burden as well as privacy risks. In this paper, we propose a simple yet effective method for noise-tolerant FL based on the well-established co-training framework. Our method leverages the inherent discrepancy in the learning ability of the local and global models in FL, which can be regarded as two complementary views. By iteratively exchanging samples with their high confident predictions, the two models “teach each other” to suppress the influence of noisy labels. The proposed scheme enjoys the benefit of overhead cost-free and can serve as a robust and efficient baseline for noise-tolerant federated learning. Experimental results demonstrate that our method outperforms existing approaches, highlighting the superiority of our method.

关键词： Federated learning noise-label learning privacy-preserving machine learning edge intelligence distributed machine learning

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BloomDT-An improved privacy-preserving decision tree inference scheme

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Journal of Information and Intelligence 2024年第2期2卷 130-147页

作者： Sean Lalla Rongxing Lu Yunguo Guan Songnian Zhang Canadian Institute for Cybersecurity(CIC) Faculty of Computer ScienceUniversity of New Brunswick(UNB)Fredericton E3B 5A3Canada

Outsourcing decision tree models to cloud servers can allow model providers to distribute their models at scale without purchasing dedicated hardware for model ***,model providers may be forced to disclose private model details when hosting their models in the *** to the time and monetary investments associated with model training,model providers may be reluctant to host their models in the cloud due to these privacy ***,clients may be reluctant to use these outsourced models because their private queries or their results may be disclosed to the cloud *** this paper,we propose BloomDT,a privacy-preserving scheme for decision tree inference,which uses Bloom filters to hide the original decision tree's structure,the threshold values of each node,and the order in which features are tested while maintaining reliable classification results that are secure even if the cloud servers *** scheme's security and performance are verified through rigorous testing and analysis.

关键词： Decision tree privacy-preserving machine learning Bloom filter Model outsourcing

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Federated learning with privacy-preserving and Model IP-right-protection

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machine Intelligence Research 2023年第1期20卷 19-37页

作者： Qiang Yang Anbu Huang Lixin Fan Chee Seng Chan Jian Han Lim Kam Woh Ng Ding Sheng Ong Bowen Li WeBank Shenzhen 518057China Hong Kong University of Science and Technology Hong Kong 999077China University of Malaya Kuala Lumpur 50603Malaysia University of Surrey Guildford GU27XHUK University of Aberystwyth Wales SY233DDUK Shanghai Jiao Tong University Shanghai 200240China

In the past decades,artificial intelligence(AI)has achieved unprecedented success,where statistical models become the central entity in ***,the centralized training and inference paradigm for building and using these models is facing more and more privacy and legal *** bridge the gap between data privacy and the need for data fusion,an emerging AI paradigm feder-ated learning(FL)has emerged as an approach for solving data silos and data privacy *** on secure distributed AI,feder-ated learning emphasizes data security throughout the lifecycle,which includes the following steps:data preprocessing,training,evalu-ation,and *** keeps data security by using methods,such as secure multi-party computation(MPC),differential privacy,and hardware solutions,to build and use distributed multiple-party machine-learning systems and statistical models over different data *** data privacy concerns,we argue that the concept of“model”matters,when developing and deploying federated models,they are easy to expose to various kinds of risks including plagiarism,illegal copy,and *** address these issues,we introduce FedIPR,a novel ownership verification scheme,by embedding watermarks into FL models to verify the ownership of FL models and protect model intellectual property rights(IPR or IP-right for short).While security is at the core of FL,there are still many articles re-ferred to distributed machine learning with no security guarantee as“federated learning”,which are not satisfied with the FL definition supposed to *** this end,in this paper,we reiterate the concept of federated learning and propose secure federated learning(SFL),where the ultimate goal is to build trustworthy and safe AI with strong privacy-preserving and *** provide a com-prehensive overview of existing works,including threats,attacks,and defenses in each phase of SFL from the lifecycle perspective.

关键词： Federated learning privacy-preserving machine learning security decentralized learning intellectual property protection

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Concretely efficient secure multi-party computation protocols: survey and more

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Security and Safety 2022年第1期1卷 48-90页

作者： Dengguo Feng Kang Yang State Key Laboratory of Cryptology State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences

Secure multi-party computation(MPC) allows a set of parties to jointly compute a function on their private inputs, and reveals nothing but the output of the function. In the last decade, MPC has rapidly moved from a purely theoretical study to an object of practical interest, with a growing interest in practical applications such as privacy-preserving machine learning(PPML). In this paper, we comprehensively survey existing work on concretely efficient MPC protocols with both semi-honest and malicious security, in both dishonestmajority and honest-majority settings. We focus on considering the notion of security with abort, meaning that corrupted parties could prevent honest parties from receiving output after they receive output. We present high-level ideas of the basic and key approaches for designing different styles of MPC protocols and the crucial building blocks of MPC. For MPC applications, we compare the known PPML protocols built on MPC, and describe the efficiency of private inference and training for the state-of-the-art PPML protocols. Furthermore, we summarize several challenges and open problems to break though the efficiency of MPC protocols as well as some interesting future work that is worth being addressed. This survey aims to provide the recent development and key approaches of MPC to researchers,who are interested in knowing, improving, and applying concretely efficient MPC protocols.

关键词： Secure multi-party computation privacy-preserving machine learning Secret sharings Garbled circuits Oblivious transfer and its arithmetic generalization

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Defense against Membership Inference Attack Applying Domain Adaptation with Addictive Noise

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Journal of Computer and Communications 2021年第5期9卷 92-108页

作者： Hongwei Huang College of Information Science and Technology Jinan University Guangzhou China

Deep learning can train models from a dataset to solve tasks. Although deep learning has attracted much interest owing to the excellent performance, security issues are gradually exposed. Deep learning may be prone to the membership inference attack, where the attacker can determine the membership of a given sample. In this paper, we propose a new defense mechanism against membership inference: NoiseDA. In our proposal, a model is not directly trained on a sensitive dataset to alleviate the threat of membership inference attack by leveraging domain adaptation. Besides, a module called Feature Crafter has been designed to reduce the necessary training dataset from 2 to 1, which creates features for domain adaptation training using noise addictive mechanisms. Our experiments have shown that, with the noises properly added by Feature Crafter, our proposal can reduce the success of membership inference with a controllable utility loss.

关键词： privacy-preserving machine learning Membership Inference Attack Domain Adaptation Deep learning

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Concretely ecient secure multi-party computation protocols:survey and mor

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Security and Safety 2022年第1期1卷 47-89页

作者： Dengguo Feng Kang Yang State Key Laboratory of Cryptology Beijing 100878China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the *** the last decade,MPC has rapidly moved from a purely theoretical study to an object of practical interest,with a growing interest in practical applications such as privacy-preserving machine learning(PPML).In this paper,we comprehensively survey existing work on concretely ecient MPC protocols with both semi-honest and malicious security,in both dishonest-majority and honest-majority *** focus on considering the notion of security with abort,meaning that corrupted parties could prevent honest parties from receiving output after they receive *** present high-level ideas of the basic and key approaches for designing di erent styles of MPC protocols and the crucial building blocks of *** MPC applications,we compare the known PPML protocols built on MPC,and describe the eciency of private inference and training for the state-of-the-art PPML ***-more,we summarize several challenges and open problems to break though the eciency of MPC protocols as well as some interesting future work that is worth being *** survey aims to provide the recent development and key approaches of MPC to researchers,who are interested in knowing,improving,and applying concretely ecient MPC protocols.

关键词： Secure multi-party computation privacy-preserving machine learning Secret sharings Garbled circuits Oblivious transfer and its arithmetic generalization

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