IEEE Journal on Emerging and Selected Topics in Circuits and Systems最新文献_第4页

Auditing and Generating Synthetic Data With Controllable Trust Trade-Offs 审计和生成具有可控信任权衡的合成数据

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-10-10 DOI: 10.1109/JETCAS.2024.3477976

Brian Belgodere;Pierre Dognin;Adam Ivankay;Igor Melnyk;Youssef Mroueh;Aleksandra Mojsilović;Jiri Navratil;Apoorva Nitsure;Inkit Padhi;Mattia Rigotti;Jerret Ross;Yair Schiff;Radhika Vedpathak;Richard A. Young

{"title":"Auditing and Generating Synthetic Data With Controllable Trust Trade-Offs","authors":"Brian Belgodere;Pierre Dognin;Adam Ivankay;Igor Melnyk;Youssef Mroueh;Aleksandra Mojsilović;Jiri Navratil;Apoorva Nitsure;Inkit Padhi;Mattia Rigotti;Jerret Ross;Yair Schiff;Radhika Vedpathak;Richard A. Young","doi":"10.1109/JETCAS.2024.3477976","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3477976","url":null,"abstract":"Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have emerged to address these issues by enabling a paradigm that relies on generative AI models to generate unbiased, privacy-preserving data while maintaining fidelity to the original data. However, assessing the trustworthiness of synthetic datasets and models is a critical challenge. We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensuring fidelity to the source data, and assessing utility, robustness, and privacy preservation. We demonstrate our framework’s effectiveness by auditing various generative models across diverse use cases like education, healthcare, banking, and human resources, spanning different data modalities such as tabular, time-series, vision, and natural language. This holistic assessment is essential for compliance with regulatory safeguards. We introduce a trustworthiness index to rank synthetic datasets based on their safeguards trade-offs. Furthermore, we present a trustworthiness-driven model selection and cross-validation process during training, exemplified with “TrustFormers” across various data types. This approach allows for controllable trustworthiness trade-offs in synthetic data creation. Our auditing framework fosters collaboration among stakeholders, including data scientists, governance experts, internal reviewers, external certifiers, and regulators. This transparent reporting should become a standard practice to prevent bias, discrimination, and privacy violations, ensuring compliance with policies and providing accountability, safety, and performance guarantees.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 4","pages":"773-788"},"PeriodicalIF":3.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Overview of Trustworthy AI: Advances in IP Protection, Privacy-Preserving Federated Learning, Security Verification, and GAI Safety Alignment 可信人工智能概述：知识产权保护、保护隐私的联合学习、安全验证和 GAI 安全调整方面的进展

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-10-09 DOI: 10.1109/JETCAS.2024.3477348

Yue Zheng;Chip-Hong Chang;Shih-Hsu Huang;Pin-Yu Chen;Stjepan Picek

{"title":"An Overview of Trustworthy AI: Advances in IP Protection, Privacy-Preserving Federated Learning, Security Verification, and GAI Safety Alignment","authors":"Yue Zheng;Chip-Hong Chang;Shih-Hsu Huang;Pin-Yu Chen;Stjepan Picek","doi":"10.1109/JETCAS.2024.3477348","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3477348","url":null,"abstract":"AI has undergone a remarkable evolution journey marked by groundbreaking milestones. Like any powerful tool, it can be turned into a weapon for devastation in the wrong hands. Understanding that no model is perfect, trustworthy AI is initiated with an intuitive aim to mitigate the harm it can inflict on people and society by prioritizing socially responsible AI ideation, design, development, and deployment towards effecting positive changes. The scope of trustworthy AI is encompassing, covering qualities such as safety, security, privacy, transparency, explainability, fairness, impartiality, robustness, reliability, and accountability. This overview paper anchors on recent advances in four research hotspots of trustworthy AI with compelling and challenging security, privacy, and safety issues. The topics discussed include the intellectual property protection of deep learning and generative models, the trustworthiness of federated learning, verification and testing tools of AI systems, and the safety alignment of generative AI systems. Through this comprehensive review, we aim to provide readers with an overview of the most up-to-date research problems and solutions. By presenting the rapidly evolving factors and constraints that motivate the emerging attack and defense strategies throughout the AI life-cycle, we hope to inspire more research effort into guiding AI technologies towards beneficial purposes with greater robustness against malicious use intent.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 4","pages":"582-607"},"PeriodicalIF":3.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10711270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diffense: Defense Against Backdoor Attacks on Deep Neural Networks With Latent Diffusion 差分：基于潜在扩散的深度神经网络后门攻击防御

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-09-27 DOI: 10.1109/JETCAS.2024.3469377

Bowen Hu;Chip-Hong Chang

{"title":"Diffense: Defense Against Backdoor Attacks on Deep Neural Networks With Latent Diffusion","authors":"Bowen Hu;Chip-Hong Chang","doi":"10.1109/JETCAS.2024.3469377","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3469377","url":null,"abstract":"As deep neural network (DNN) models are used in a wide variety of applications, their security has attracted considerable attention. Among the known security vulnerabilities, backdoor attacks have become the most notorious threat to users of pre-trained DNNs and machine learning services. Such attacks manipulate the training data or training process in such a way that the trained model produces a false output to an input that carries a specific trigger, but behaves normally otherwise. In this work, we propose Diffense, a method for detecting such malicious inputs based on the distribution of the latent feature maps to clean input samples of the possibly infected target DNN. By learning the feature map distribution using the diffusion model and sampling from the model under the guidance of the data to be inspected, backdoor attack data can be detected by its distance from the sampled result. Diffense does not require knowledge about the structure, weights, and training data of the target DNN model, nor does it need to be aware of the backdoor attack method. Diffense is non-intrusive. The accuracy of the target model to clean inputs will not be affected by Diffense and the inference service can be run uninterruptedly with Diffense. Extensive experiments were conducted on DNNs trained for MNIST, CIFRA-10, GSTRB, ImageNet-10, LSUN Object and LSUN Scene applications to show that the attack success rates of diverse backdoor attacks, including BadNets, IDBA, WaNet, ISSBA and HTBA, can be significantly suppressed by Diffense. The results generally exceed the performances of existing backdoor mitigation methods, including those that require model modifications or prerequisite knowledge of model weights or attack samples.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 4","pages":"729-742"},"PeriodicalIF":3.7,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Artificial Intelligence With Novel Matrix Transformations and Homomorphic Encryption 利用新型矩阵变换和同态加密实现高效人工智能

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-09-24 DOI: 10.1109/JETCAS.2024.3466849

Quoc Bao Phan;Tuy Tan Nguyen

{"title":"Efficient Artificial Intelligence With Novel Matrix Transformations and Homomorphic Encryption","authors":"Quoc Bao Phan;Tuy Tan Nguyen","doi":"10.1109/JETCAS.2024.3466849","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3466849","url":null,"abstract":"This paper addresses the challenges of data privacy and computational efficiency in artificial intelligence (AI) models by proposing a novel hybrid model that combines homomorphic encryption (HE) with AI to enhance security while maintaining learning accuracy. The novelty of our model lies in the introduction of a new matrix transformation technique that ensures compatibility with both HE algorithms and AI model weight matrices, significantly improving computational efficiency. Furthermore, we present a first-of-its-kind mathematical proof of convergence for integrating HE into AI models using the adaptive moment estimation optimization algorithm. The effectiveness and practicality of our approach for training on encrypted data are showcased through comprehensive evaluations of well-known datasets for air pollution forecasting and forest fire detection. These successful results demonstrate high model performance, with nearly 1 R-squared for air pollution forecasting and 99% accuracy for forest fire detection. Additionally, our approach achieves a reduction of up to 90% in data storage and a tenfold increase in speed compared to models that do not use the matrix transformation method. Our primary contribution lies in enhancing the security, efficiency, and dependability of AI models, particularly when dealing with sensitive data.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 4","pages":"717-728"},"PeriodicalIF":3.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Re_useVFL: Reuse of Parameters-Based Verifiable Federated Learning With Privacy Preservation Using Gradient Sparsification Re_useVFL：使用梯度稀疏化重用基于参数的可验证联合学习并保护隐私

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-09-19 DOI: 10.1109/JETCAS.2024.3463738

Ningxin He;Tiegang Gao;Chuan Zhou

{"title":"Re_useVFL: Reuse of Parameters-Based Verifiable Federated Learning With Privacy Preservation Using Gradient Sparsification","authors":"Ningxin He;Tiegang Gao;Chuan Zhou","doi":"10.1109/JETCAS.2024.3463738","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3463738","url":null,"abstract":"Federated learning (FL) exhibits promising potential in the Industrial Internet of Things (IIoT) as it allows multiple institutions to collaboratively train a global model without sharing local data. However, there are still many privacy and security concerns in FL systems. The cloud server responsible for aggregating model parameters may be malicious, and it may distribute manipulated aggregation results that could launch nefarious attacks. Additionally, industrial agents may provide incomplete parameters, negatively impacting the global model’s performance. To address these issues, we introduce Re_useVFL, an efficient privacy-preserving full-process FL verification scheme. It integrates BLS-based signature verification, adaptive gradient sparsification (AdaGS), and Multi-Key CKKS encryption (MK-CKKS). Our scheme ensures the integrity of agents-uploaded parameters, the correctness of the cloud server’s aggregation results, and the consistency verification of distributed results, thereby providing comprehensive verification across the entire FL process. It also maintains validation accuracy even with some agents dropout during computation. The AdaGS algorithm notably reduces validation overhead by optimizing parameter sparsification and reuse. Additionally, employing MK-CKKS to protect agents privacy and prevent agent and server collusion. Our experiments on three datasets confirm that Re_useVFL achieves lower validation resource overhead compared to existing methods, demonstrating its practical effectiveness.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 4","pages":"647-660"},"PeriodicalIF":3.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Circuits and Systems Society Information 电气和电子工程师学会电路与系统协会信息

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-09-16 DOI: 10.1109/JETCAS.2024.3450049

引用次数: 0

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Publication Information 电气和电子工程师学会电路与系统新专题与选题期刊》出版信息

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-09-16 DOI: 10.1109/JETCAS.2024.3450055

引用次数: 0

Guest Editorial Chip and Package-Scale Communication-Aware Architectures for General-Purpose, Domain-Specific, and Quantum Computing Systems 特邀编辑通用、特定领域和量子计算系统的芯片和封装级通信感知架构

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-09-16 DOI: 10.1109/JETCAS.2024.3445208

Abhijit Das;Maurizio Palesi;John Kim;Partha Pratim Pande

{"title":"Guest Editorial Chip and Package-Scale Communication-Aware Architectures for General-Purpose, Domain-Specific, and Quantum Computing Systems","authors":"Abhijit Das;Maurizio Palesi;John Kim;Partha Pratim Pande","doi":"10.1109/JETCAS.2024.3445208","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3445208","url":null,"abstract":"This Special Issue of IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) is devoted to advancing the field of chip and package-scale communications across diverse computing domains, bridging academic research and industrial innovation. As we enter a new golden age of computer architecture, marked by both challenges and opportunities, the anticipated end of Moore’s law necessitates reimagining the future of computing systems as we approach the physical limits of transistors. Three leading approaches to address these challenges include the chiplet paradigm, domain-specific customization, and quantum computing. However, these architectural and technological innovations have shifted the primary bottleneck from computation to communication. Consequently, on-chip and on-package communication now play a critical role in determining the performance, efficiency, and scalability of general-purpose, domain-specific, and quantum computing systems. Their ever-growing importance has garnered significant attention from both academia and industry.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 3","pages":"349-353"},"PeriodicalIF":3.7,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Information for Authors IEEE 《电路与系统新兴选题》期刊作者须知

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-09-16 DOI: 10.1109/JETCAS.2024.3450053

引用次数: 0

Modeling the Effect of SEUs on the Configuration Memory of SRAM-FPGA-Based CNN Accelerators 模拟 SEU 对基于 SRAM-FPGA 的 CNN 加速器配置存储器的影响

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-09-16 DOI: 10.1109/JETCAS.2024.3460792

Zhen Gao;Jiaqi Feng;Shihui Gao;Qiang Liu;Guangjun Ge;Yu Wang;Pedro Reviriego

{"title":"Modeling the Effect of SEUs on the Configuration Memory of SRAM-FPGA-Based CNN Accelerators","authors":"Zhen Gao;Jiaqi Feng;Shihui Gao;Qiang Liu;Guangjun Ge;Yu Wang;Pedro Reviriego","doi":"10.1109/JETCAS.2024.3460792","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3460792","url":null,"abstract":"Convolutional Neural Networks (CNNs) are widely used in computer vision applications. SRAM based Field Programmable Gate Arrays (SRAM-FPGAs) are popular for the acceleration of CNNs. Since SRAM-FPGAs are prone to soft errors, the reliability evaluation and efficient fault tolerance design become very important for the use of FPGA-based CNNs in safety critical scenarios. Hardware based fault injection is an effective approach for the reliability evaluation, and the results can provide valuable references for the fault tolerance design. However, the complexity of building a fault injection platform poses a big obstacle for researchers working on the fault tolerance design. To remove this obstacle, this paper first performs a complete reliability evaluation for errors on the configuration memory of the FPGA based CNN accelerators, and then studies the impact of errors on the output feature maps of each layer. Based on the statistical analysis, we propose several fault models for the effect of SEUs on the configuration memory of the FPGA based CNN accelerators, and build a software simulator based on the fault models. Experiments show that the evaluation results based on the software simulator are very close to those from the hardware fault injections. Therefore, the proposed fault models and simulator can facilitate the fault tolerance design and reliability evaluation of CNN accelerators.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 4","pages":"799-810"},"PeriodicalIF":3.7,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0