检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,487 篇 会议
26 册 图书
6 篇 期刊文献

馆藏范围

1,519 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,148 篇 工学
- 1,073 篇 计算机科学与技术...
- 569 篇 软件工程
- 311 篇 信息与通信工程
- 139 篇 电气工程
- 118 篇 控制科学与工程
- 66 篇 安全科学与工程
- 44 篇 电子科学与技术（可...
- 44 篇 网络空间安全
- 41 篇 动力工程及工程热...
- 38 篇 生物医学工程（可授...
- 34 篇 生物工程
- 33 篇 机械工程
- 29 篇 建筑学
- 28 篇 交通运输工程
- 24 篇 仪器科学与技术
- 20 篇 环境科学与工程（可...
237 篇 理学
- 168 篇 数学
- 42 篇 生物学
- 28 篇 物理学
- 22 篇 统计学（可授理学、...
- 21 篇 系统科学
219 篇 管理学
- 145 篇 管理科学与工程(可...
- 84 篇 图书情报与档案管...
- 70 篇 工商管理
74 篇 法学
- 44 篇 社会学
- 31 篇 法学
62 篇 医学
- 50 篇 临床医学
- 47 篇 基础医学(可授医学...
- 40 篇 公共卫生与预防医...
25 篇 经济学
- 25 篇 应用经济学
15 篇 教育学
7 篇 军事学
5 篇 农学
2 篇 文学

主题

147 篇 privacy
109 篇 security
75 篇 data privacy
68 篇 blockchain
46 篇 internet of thin...
38 篇 differential pri...
38 篇 machine learning
36 篇 network security
36 篇 cryptography
33 篇 cloud computing
32 篇 authentication
29 篇 access control
27 篇 big data
24 篇 sensitive data
23 篇 data security
22 篇 deep learning
22 篇 data mining
22 篇 federated learni...
21 篇 artificial intel...
19 篇 iot

机构

6 篇 north carolina s...
6 篇 rhein westfal th...
5 篇 carnegie mellon ...
5 篇 school of cyber ...
4 篇 nanjing audit un...
4 篇 stevens inst tec...
4 篇 univ michigan an...
4 篇 fraunhofer aisec...
4 篇 univ texas dalla...
4 篇 institute of inf...
4 篇 chinese acad sci...
3 篇 singapore inst t...
3 篇 chinese acad sci...
3 篇 university of mi...
3 篇 umass boston bos...
3 篇 east china norma...
3 篇 beijing univ pos...
3 篇 university of el...
3 篇 carnegie mellon ...
3 篇 tech univ dresde...

作者

9 篇 bertino elisa
6 篇 enck william
6 篇 ghinita gabriel
6 篇 sandhu ravi
5 篇 meyer ulrike
5 篇 kantarcioglu mur...
4 篇 conti mauro
4 篇 felber pascal
4 篇 mehrotra sharad
4 篇 li lei
3 篇 gupta maanak
3 篇 jianying zhou
3 篇 zhang lei
3 篇 lee adam j.
3 篇 li jin
3 篇 balachandran viv...
3 篇 patwa farhan
3 篇 xiong gang
3 篇 saxena nitesh
3 篇 sallam asmaa

语言

1,390 篇 英文
127 篇 其他
7 篇 中文

检索条件"任意字段=Proceedings of the 5th ACM Conference on Data and Application Security and Privacy"

共 1519 条记录，以下是161-170 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Decentralized Parallel Blockchain Agricultural Product Traceability System security Analysis 5

Decentralized Parallel Blockchain Agricultural Product Trace...

引用

5th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Fenglin, Sun Guangxi Normal University Qixing District Guangxi Guilin City541000 China

ISBN: (纸本)9798350395235

this research work presents a decentralized parallel blockchain-based agricultural product traceability system, which aims to enhance information security and data model efficiency, and achieve real-time tracking of agricultural products. through an in-depth analysis of decentralized blockchain applications, the system adopts distributed ledger processing for low-cost transactions. the Digital Promise technology ensures the credibility of the data transmission and privacy protection. the proposed decentralized parallel blockchain structure shows significant traceability results, ensuring transparent traceability of agricultural products. Experimental results demonstrate the effectiveness of the system, improving food safety and quality management. the decentralized nature reduces the risk of data tampering, thereby enhancing public trust. Experimental results show short query response times, efficient data transfers, and zero failures, highlighting the system's excellent performance in handling traceability queries. these results underscore the remarkable advantages of blockchain technology in increasing the efficiency and reliability of traceability systems. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

proceedings - 2024 5th International conference on Computer, Big data and Artificial Intelligence, ICCBD+AI 2024

Proceedings - 2024 5th International Conference on Computer,...

引用

5th International conference on Computer, Big data and Artificial Intelligence, ICCBD+AI 2024

ISBN: (纸本)9798331533991

the proceedings contain 109 papers. the topics discussed include: optimization and application of high-performance algorithm under data governance framework;constructing fine-tuning datasets for bid-generation-model using title-content similarity;network security threat intelligence modeling based on knowledge graph;early warning method for drilling overflow based on temporal deep feature cross;remote sensing image change detection based on similarity sensing network;a hybrid feature integration network for robust person re-identification;informed similarity transfer: a scientific machine learning approach for meteorological data imputation;research on matrix multiplication optimization and deployment method for heterogeneous platforms;robust ANN searching for dynamic vector streams without rebuilding index;and research on rebuilding the ship’s finite element model from nodal data with triangular mesh algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

the application of Edge Computing in the Operation Control of Integrated Energy Systems 5

The Application of Edge Computing in the Operation Control o...

引用

5th International conference on Clean Energy and Electric Power Engineering, ICCEPE 2024

作者： Yang, Yuqiang Li, Lei Zhao, Qiming Lu, Pengfei State Grid Zhejiang Electric Power Company LTD. Marketing Department Hangzhou China

ISBN: (纸本)9798350390315

With the development of new technologies such as big data, cloud computing, Internet of things, mobile Internet, and artificial intelligence, the connotation of integrated energy services continues to expand. Integrated energy system services have many management objects, handle a large amount of data, and have high privacy and security requirements. the traditional centralized cloud computing architecture is difficult to meet the application requirements. the distributed edge computing technology provides a new idea to solve this problem. Starting from the actual business requirements, the difficulties and challenges faced by the operation control of the integrated energy system are analyzed, and a hierarchical collaborative control architecture of the integrated energy system based on the Internet of things is constructed. the applicability and value points of edge computing technology are analyzed from the four levels of the perception layer, network layer, platform layer, and application layer. then, taking the industrial park as an example, a regional integrated energy system operation control model and solution method based on multilateral collaboration are proposed. Finally, the application prospects of edge computing in integrated energy services are prospected. © 2024 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

A Meticulous Range of Forensics Tech Advancement in the field of IoT, IIoT and MIoT 5

A Meticulous Range of Forensics Tech Advancement in the fiel...

引用

5th IEEE International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Matta, Priya Kamalraj, R. Saxena, Kumud Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Computer Science Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

Wide application of millions of connected, intelligent, and flexible gadgets in critical infrastructures like medical facilities, public transit, sustainability, and home automation has been driven by latest expansions in intelligence technology and hardware. Clients profit from consistency and accessibility when data is transmitted across the internet deprived of the need for any kind of person to person or person-to-computer communication however this additionally creates new prospects for hackers and increases a number of complicated and challenging issues for the area of online forensics. Regardless of the fact that IoT records may be a valuable evidence source, forensics professionals must deal with a wide range of issues, starting the massive diversity of IoT devices and unrecognized setups to the multi-tenant platforms of cloud and the ensuing multijurisdictional lawsuits. Encryption at all levels, which compromises users' rights regarding confidentiality for the benefit of forensics study, is another difficulty. the aim of this work is to recognize and address the issues connected through the complex procedure of IoT based inquiries, particularly the privacy and security problems. the study also examines the application of IoT forensics across different industries. © 2024 IEEE.

关键词： IoT (Internet of things) IoT forensics privacy issues and digital forensics security issues

来源：评论

学校读者我要写书评

暂无评论

GPU-based Private Information Retrieval for On-Device Machine Learning Inference 24

GPU-based Private Information Retrieval for On-Device Machin...

引用

29th acm International conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Lam, Maximilian Johnson, Jeff Xiong, Wenjie Maeng, Kiwan Gupta, Udit Li, Yang Lai, Liangzhen Leontiadis, Ilias Rhu, Minsoo Lee, Hsien-Hsin S. Reddi, Vijay Janapa Wei, Gu-Yeon Brooks, David Suh, G. Edward Meta AI Menlo Pk CA 94025 USA Harvard Univ Cambridge MA 02138 USA Virginia Tech Blacksburg VA USA Penn State University Pk PA USA Intel Santa Clara CA USA Cornell Univ Ithaca NY USA

ISBN: (纸本)9798400703720

On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on embedding tables that are too large to be stored on-device. In particular, recommendation models typically use multiple embedding tables each on the order of 1-10 GBs of data, making them impractical to store on-device. To over-come this barrier, we propose the use of private information retrieval (PIR) to efficiently and privately retrieve embeddings from servers without sharing any private information. As off-the-shelf PIR algorithms are usually too computationally intensive to directly use for latency-sensitive inference tasks, we 1) propose novel GPU-based acceleration of PIR, and 2) co-design PIR with the downstream ML application to obtain further speedup. Our GPU acceleration strategy improves system throughput by more than 20x over an optimized CPU PIR implementation, and our PIR-ML co-design provides an over 5x additional throughput improvement at fixed model quality. Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to 100, 000 queries per second a > 100x throughput improvement over a CPU-based baseline-while maintaining model accuracy.

关键词： privacy security cryptography machine learning GPU performance

来源：评论

学校读者我要写书评

暂无评论

the Future of privacy: A Review on AI's Role in Shaping data security 5

The Future of Privacy: A Review on AI's Role in Shaping Data...

引用

5th International conference in Electronic Engineering, Information Technology and Education, EEITE 2024

作者： Vardalachakis, Marios Tampouratzis, Manolis Papadakis, Nikos Vasilakis, Manos GR Heraklion71004 Greece FORTH Nikolaou Plastira 100 Vouton Heraklion GR Vassilika70013 Greece

ISBN: (纸本)9798350372878

the rapid adoption of Artificial Intelligence (AI) technological advances marked an era of exceptional growth, significantly converting how we interact with the world of technology. But this adoption has arrived at an enormous cost: the gradual loss of private life. the study starts with an indepth investigation into the complicated and modifying relationship between AI and privacy, acquiring information via case studies in the natural environment. One prominent case study is deploying AI technology to recognize faces in public spaces. Issues around illicit tracking and privacy intrusions have grown as governments and business entities regularly employ such technologies for recognition and protection. Assessing the challenges and findings associated with such deployments offers the conceptual debate a practical setting. It explores the various challenges that arise from AIdriven innovations and offers fresh strategies for maintaining and safeguarding individual privacy concerns in emerging data-centric circumstances. It emphasizes AI's dual role as a catalyst for outstanding outcomes and a potential risk to personal privacy. Finally, this study is interested in contributing to the current discussion on AI and privacy, by providing an in-depth examination of the difficulties that might be utilized by lawmakers, stakeholders in the industry, as well as individuals alike. As humanity moves into a scenario where AI encompasses every aspect of daily life, the study acts as a guide for managing the evolving terrain concerning confidentiality in the era of AI, assisting appropriate AI advancement and preserving human privacy rights. © 2024 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Let's Hide from LLMs: An Adaptive Contextual privacy Preservation Method for Time Series data 6

Let's Hide from LLMs: An Adaptive Contextual Privacy Preserv...

引用

6th Artificial Intelligence and Cloud Computing conference (AICCC) / 5th Asia Digital Image Processing conference

作者： Rehman, Ubaid Ur Hussain, Musarrat Nguyen, Tri D. T. Lee, Sungyoung Kyung Hee Univ Global Campus Yongin South Korea

ISBN: (纸本)9798400716225

the emergence of the Internet of things (IoT) has evolved various application areas, such as healthcare, smart energy management, and autonomous vehicles. these devices continuously transmit time-series data that can be utilized by a variety of applications to provide personalized services. Recently, Large Language Models (LLMs) have been widely adopted in these application areas to input time-series data into prompts for in-context learning and to retrieve relevant responses accordingly. the time-series data contains sensitive information, and its processing can lead to privacy concerns. Several solutions have been proposed in the literature using differential privacy, which protects single data points or batch-wise privacy preservation through manual configuration of the privacy parameter (epsilon). In this paper, we propose an adaptive contextual privacy preservation method that analyzes the data attributes required for specific application services, acting as context. It then identifies sensitive attributes and adaptively selects the value of epsilon for each data attribute to maintain a balance between privacy and service requirements. the proposed approach was evaluated using power consumption and solar power generation datasets. the results show that the proposed approach dynamically selects the privacy parameter for each data attribute. Moreover, the original and anonymized data were fed into the prompt to assess the textual responses generated by LLM. the results show that our proposed approach achieved an average degree of semantic similarity score of 94.5% for power consumption data and 95.23% for solar power generation data.

关键词： Large Language Models privacy Preservation Differential privacy Time Series data

来源：评论

学校读者我要写书评

暂无评论

Personal Document security Tool for Cloud application 5

Personal Document Security Tool for Cloud Application

引用

5th International conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Mahmood, Bdoor Alaa College of Computer Science and Information Technology University of Kerbala Karbala56001 Iraq

ISBN: (纸本)9798350363739

the recent advancement in cloud storage has spurred the creation of more methods in developing the personal data security features in both applications and personal document storage. Personal document storage can be divided into different segments based on the types of document and the usage of the document in cloud storage. this research focuses on personal document storage used by individuals for their personal use only. the method involves developing a simple and easy-to-use security tool with secured features and efficient algorithm encryption and decryption with reliable speed for large files, such as Word files and pictures. In this research, more than one algorithm will be tested using graphical user interface, which has encryption and decryption through a private cloud computing server. © 2024 IEEE.

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Scalability of Knowledge Discovery Services for Massive data Mining on Clouds 5

Enhancing the Scalability of Knowledge Discovery Services fo...

引用

5th International conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sajjan, Vijayakumar Kumar, Jayam Pradeep Kalvala, Vikram Domala, Narsaiah Kumar, B. Kiran Pavan Kumar Hota, C.P. Malla Reddy University Department of CSE Hyderabad India Chaitanya Deemed to Be University Department of ECE Hyderabad India MLR Institute of Technology Department of ECE Hyderabad India Shri Vishnu Engineering College for Women Department of Artificial Intelligence Bhimavaram India

ISBN: (纸本)9798331518097

the traditional data mining techniques do not work well when it comes to the big data and the tremendous growth rate of the digital data also becomes a major challenge to the discovery of knowledge. Cloud computing offers an ideal solution to computationally and storage intensive tasks with scalable and on-demand services that can process big data at short notice. this paper reviews the current models that employ cloud structure for distributed data mining as well as models like CosDic and the data Mining Cloud Framework (DMCF). the way of processing large datasets is also represented well by the Map Reduce model where it is possible to implement the data processing in parallel on several nodes. Despite the fact that cloud-based systems are very useful when it comes to the management of large amounts of data, challenges such as privacy, data security and data coordination still persist. these innovative methodologies include Edge Computing and Federated Learning that are anticipated to solve the mentioned issues by enhancing the speed of data processing and its safety. thus, scalable cloud data mining to meet growing needs of knowledge discovery in big data contexts will have to develop new privacy protection mechanisms, integration of cloud models, and complex algorithms. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Detecting data Manipulation in Electric Vehicle Charging Stations Using Machine Learning Algorithm 5

Detecting Data Manipulation in Electric Vehicle Charging Sta...

引用

5th International conference on Sustainable Communication Networks and application, ICSCNA 2024

作者： Akhila, Jannavarapu Vani Bhargavi, Maridu Manikanta, Mondem Lakshmi, Srigakolapu Sai Mithra, Nagulapati Phanindra Raja Vignan's Foundation for Science Technology and Research Department of Computer Science And Engineering Vadlamudi AP Guntur India

ISBN: (纸本)9798331530013

A vehicle that functions purely on electricity, as opposed to normal gasoline or diesel automobiles, is known as an electric vehicle (EV). Electric vehicles are becoming extremely popular. EVs come equipped with a built-in connection that allows for features like remote control via smartphone apps, over-the-air software upgrades, and navigation. Smart charging stations are available for electric vehicles (EVs) and are capable of utilizing energy consumption and cost data to improve charging times through communication with the car. However, there are cyber security problems nowadays everywhere. Monthly pricing is one option for charging at electric charging stations. False data Injection Attacks are a frequent form of attack in which hackers alter data to their benefit. We are developing a machine learning algorithm to identify the site of the deceptive data injection in order to solve this issue. Random Forest Algorithms, RBF kernel, linear kernel, polynomial kernel, and Support Vector Machine were utilized for training and assessing the outcomes. © 2024 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共152页 << < 13 14 15 16 17 18 19 20 21 22 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：