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检索条件"任意字段=13th ACM Conference on Data and Application Security and Privacy, CODASPY 2023"

共 46 条记录，以下是31-40 订阅

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Architecture of Integrated Resource System Based on dataspace 1

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13th International conference on Computer Engineering and Networks, CENet 2023

作者： Yan, Hui Chen, Bo China Telecom Research Institute Beijing China China University of Political Science and Law Beijing China

ISBN: (数字)9789819992393

ISBN: (纸本)9789819992386

At present, there are some urgent problems in the process of data element circulation, which hinder the marketization process of data elements and block the circulation channel of data elements. It includes the difficulty of transforming original data into data resources, the difficulty of defining data ownership, the difficulty of standard governance of data assets, the difficulty of data element circulation pricing, and the difficulty of data application security and mutual trust. the emergence of these problems has brought great challenges to enterprises in large-scale distributed data governance and data mining. the architecture of this study loads the data space component of closed-loop management of data value circulation, and provides SaaS-level services, which users can subscribe to directly without considering the underlying resource deployment, network configuration and business system development and deployment. In addition, cloud, network, data are separated from each other, enterprises need to deploy these resources separately when perform business-oriented data services. that means to build a set of exclusive infrastructure services, involving cloud services, network services, privacy security services, etc. Such a format is cumbersome for enterprises, not only requires a lot of construction funds and also cause a lot of manpower and time costs. this study provides a custom PaaS-level service, the business system loaded on this architecture does not need to consider the underlying resource deployment and network configuration. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： data mining

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13th EAI International conference on Digital Forensics and Cyber Crime, ICDF2C 2022

13th EAI International Conference on Digital Forensics and C...

引用

13th EAI International conference on Digital Forensics and Cyber Crime, ICDF2C 2022

ISBN: (纸本)9783031365737

the proceedings contain 28 papers. the special focus in this conference is on Digital Forensics and Cyber Crime. the topics include: Can Image Watermarking Efficiently Protect Deep-Learning-Based Image Classifiers? – A Preliminary security Analysis of an IP-Protecting Method;digital Forensics Tool Evaluation on Deleted Files;Forensic Analysis of Webex on the iOS Platform;watch Your WeChat Wallet: Digital Forensics Approach on WeChat Payments on Android;crypto Wallet Artifact Detection on Android Devices Using Advanced Machine Learning Techniques;CSCD: A Cyber security Community Detection Scheme on Online Social Networks;shedding Light on Monopoly: Temporal Analysis of Drug Trades;extracting Spread-Spectrum Hidden data Based on Better Lattice Decoding;MQTT Traffic Collection and Forensic Analysis Framework;IoT Malicious Traffic Detection Based on FSKDE and Federated DIOT-Pysyft;crime and Incident Watch for Smart Cities: A Sensor-Based Approach;the Lightweight Botnet Detection Model Based on the Improved UNet;on the application of Active Learning to Handle data Evolution in Android Malware Detection;Volatility Custom Profiling for Automated Hybrid ELF Malware Detection;the Need for Biometric Anti-spoofing Policies: the Case of Etsy;VPnet: A Vulnerability Prioritization Approach Using Pointer Network and Deep Reinforcement Learning;are External Auditors Capable of Dealing with Cybersecurity Risks?;deep Learning-Based Detection of Cyberattacks in Software-Defined Networks;deep Learning Based Network Intrusion Detection System for Resource-Constrained Environments;poisoning-Attack Detection Using an Auto-encoder for Deep Learning Models;Attribute-Based Proxy Re-encryption with privacy Protection for Message Dissemination in VANET;PBPAFL: A Federated Learning Framework with Hybrid privacy Protection for Sensitive data.

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Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning 37

Poisoning with Cerberus: Stealthy and Colluded Backdoor Atta...

引用

37th AAAI conference on Artificial Intelligence (AAAI) / 35th conference on Innovative applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Lyu, Xiaoting Han, Yufei Wang, Wei Liu, Jingkai Wang, Bin Liu, Jiqiang Zhang, Xiangliang Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing Peoples R China INRIA Le Chesnay France Zhejiang Key Lab Multi Dimens Percept Technol App Beijing Peoples R China Univ Notre Dame Notre Dame IN USA

ISBN: (纸本)9781577358800

Are Federated Learning (FL) systems free from backdoor poisoning with the arsenal of various defense strategies deployed? this is an intriguing problem with significant practical implications regarding the utility of FL services. Despite the recent flourish of poisoning-resilient FL methods, our study shows that carefully tuning the collusion between malicious participants can minimize the trigger-induced bias of the poisoned local model from the poison-free one, which plays the key role in delivering stealthy backdoor attacks and circumventing a wide spectrum of state-of-the-art defense methods in FL. In our work, we instantiate the attack strategy by proposing a distributed backdoor attack method, namely Cerberus Poisoning (CerP). It jointly tunes the backdoor trigger and controls the poisoned model changes on each malicious participant to achieve a stealthy yet successful back-door attack against a wide spectrum of defensive mechanisms of federated learning techniques. Our extensive study on 3 large-scale benchmark datasets and 13 mainstream defensive mechanisms confirms that Cerberus Poisoning raises a significantly severe threat to the integrity and security of federated learning practices, regardless of the flourish of robust Federated Learning methods.

关键词： Arsenals

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Securing RFID-Based Attendance Management Systems: An Implementation of the AES Block Cipher Algorithm

Securing RFID-Based Attendance Management Systems: An Implem...

引用

IEEE International conference on RFID-Technology and applications (RFID-TA)

作者： Faith Ayodele Harsehaj Singh Eslam G. AbdAllah Information Systems Security and Assurance Management (ISSAM) Concordia University of Edmonton Edmonton Alberta Canada

the main feature of Radio Frequency IDentification (RFID) technology is its ability to locate, track and monitor people or objects. RFID Architecture consists of two significant components: a tag and a reader. RFID is prone to major security and privacy attacks. this research aims to demonstrate the importance of encryption in securing RFID-based attendance management systems, specifically focusing on using the Advanced Encryption Standard (AES) block cipher. the application of the cipher algorithm in encrypting the RFID tag Unique Identifier (UID) and the reader will be implemented to show how it can enhance the security of RFID tags. the proposed approach ensures secure data transmission, preventing unauthorized access and ensuring the confidentiality and integrity of attendance data. the research provides insights into the importance of encryption in securing RFID-based systems, which is critical in protecting sensitive data and ensuring the privacy and security of individuals.

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the application of Artificial Intelligence Technology in Personalized Teaching of Vocational Education

The Application of Artificial Intelligence Technology in Per...

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Shen Yanjin Liu Qian Zhang Kun Zou Ruirui Science and Technology Department Hunan Automobile Engineering Vocational College Zhuzhou City Hunan Province China

this paper aims to explore the development trends of artificial intelligence (AI) technology in personalized teaching within vocational education. the research findings indicate that AI technology can meet students' learning needs and interests through personalized learning and customized learning resource recommendations. Adaptive learning systems can automatically adjust learning content and difficulty based on students' abilities and progress, providing personalized learning support and feedback. Virtual reality and augmented reality technologies offer students rich practical training and immersive learning experiences. the advancements in natural language processing and speech recognition provide intelligent learning interactions and support. Enhanced collaborative and social learning foster cooperation and interaction among students. However, the application of AI technology in vocational education also faces challenges such as data privacy and security, technical feasibility, and acceptability. therefore, future research and practices need to pay further attention to these issues and seek solutions to ensure the sustainable development and effective implementation of AI technology in vocational education. In conclusion, AI technology holds immense potential for personalized learning and teaching in vocational education, driving the development of more intelligent, personalized, and collaborative learning approaches, enhancing students' learning outcomes and professional competencies, and providing teachers with more opportunities for instructional support and improvement. this is of great significance in cultivating talents who can adapt to future professional demands.

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CREHMA: Cache-aware REST-ful HTTP Message Authentication 20

CREHMA: Cache-aware REST-ful HTTP Message Authentication

引用

10th acm conference on data and application security and privacy (codaspy)

作者： Hoai Viet Nguyen Lo Iacono, Luigi Cologne Univ Appl Sci Cologne Germany

ISBN: (纸本)9781450371070

Scalability and security are two important elements of contemporary distributed software systems. the Web vividly shows that while complying with the constraints defined by the architectural style REST, the layered design of software with intermediate systems enables to scale at large. Intermediaries such as caches, however, interfere with the security guarantees of the industry standard for protecting data in transit on the Web, TLS, as in these circumstances the TLS channel already terminates at the intermediate system's server. For more in-depth defense strategies, service providers require message-oriented security means in addition to TLS. these are hardly available and only in the form of HTTP signature schemes that do not take caches into account either. In this paper we introduce CREHMA, a REST-ful HTTP message signature scheme that guarantees the integrity and authenticity of Web assets from end-to-end while simultaneous allowing service providers to enjoy the benefits ofWeb caches. Decisively, CREHMA achieves these guarantees without having to trust on the integrity of the cache and without requiring making changes to existingWeb caching systems. In extensive experiments we evaluated CREHMA and found that it only introduces marginal impacts on metrics such as latency and data expansion while providing integrity protection from end to end. CREHMA thus extends the possibilities of service providers to achieve an appropriate balance between scalability and security.

关键词： HTTP Web Caching REST End-to-End security Signature

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A Hypothesis Testing Approach to Sharing Logs with Confidence 20

A Hypothesis Testing Approach to Sharing Logs with Confidenc...

引用

10th acm conference on data and application security and privacy (codaspy)

作者： Long, Yunhui Xu, Le Gunter, Carl A. Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781450371070

Logs generated by systems and applications contain a wide variety of heterogeneous information that is important for performance profiling, failure detection, and security analysis. there is a strong need for sharing the logs among different parties to outsource the analysis or to improve system and security research. However, sharing logs may inadvertently leak confidential or proprietary information. Besides sensitive information that is directly saved in logs, such as user-identifiers and software versions, indirect evidence like performance metrics can also lead to the leakage of sensitive information about the physical machines and the system. In this work, we introduce a game-based definition of the risk of exposing sensitive information through released logs. We propose log indistinguishability, a property that is met only when the logs leak little information about the protected sensitive attributes. We design an end-to-end framework that allows a user to identify risk of information leakage in logs, to protect the exposure with log redaction and obfuscation, and to release the logs with a much lower risk of exposing the sensitive attribute. Our framework contains a set of statistical tests to identify violations of the log indistinguishability property and a variety of obfuscation methods to prevent the leakage of sensitive information. the framework views the log-generating process as a black-box and can therefore be applied to different systems and processes. We perform case studies on two different types of log datasets: Spark event log and hardware counters. We show that our framework is effective in preventing the leakage of the sensitive attribute with a reasonable testing time and an acceptable utility loss in logs.

关键词： indistinguishability hypothesis test log obfuscation privacy

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Explore the Transformation Space for Adversarial Images 20

Explore the Transformation Space for Adversarial Images

引用

10th acm conference on data and application security and privacy (codaspy)

作者： Chen, Jiyu Wang, David Chen, Hao Univ Calif Davis Davis CA 95616 USA Vestavia Hills High Sch Vestavia Hills AL USA

ISBN: (纸本)9781450371070

Deep learning models are vulnerable to adversarial examples. Most of current adversarial attacks add pixel-wise perturbations restricted to some L-p -norm, and defense models are evaluated also on adversarial examples restricted inside L-p-norm balls. However, we wish to explore adversarial examples exist beyond L-p-norm balls and their implications for attacks and defenses. In this paper, we focus on adversarial images generated by transformations. We start with color transformation and propose two gradient-based attacks. Since L-p-norm is inappropriate for measuring image quality in the transformation space, we use the similarity between transformations and the Structural Similarity Index. Next, we explore a larger transformation space consisting of combinations of color and affine transformations. We evaluate our transformation attacks on three data sets - CIFAR10, SVHN, and ImageNet - and their corresponding models. Finally, we perform retraining defenses to evaluate the strength of our attacks. the results show that transformation attacks are powerful. they find high-quality adversarial images that have higher transferability and misclassification rates than C&W's L-p attacks, especially at high confidence levels. they are also significantly harder to defend against by retraining than C&W's L-p attacks. More importantly, exploring different attack spaces makes it more challenging to train a universally robust model.

关键词： Adversarial attacks Image transformation Deep learning security

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CURIE: Policy-based Secure data Exchange 19

CURIE: Policy-based Secure Data Exchange

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9th acm conference on data and application security and privacy (codaspy)

作者： Celik, Z. Berkay Acar, Abbas Aksu, Hidayet Atsley, Ryan She McDaniel, Patrick Uluagac, A. Selcuk Penn State Univ Dept CSE SIIS Lab University Pk PA 16802 USA Florida Int Univ Dept ECE CPS Secur Lab Miami FL 33199 USA

ISBN: (纸本)9781450360999

data sharing among partners-users, companies, organizations-is crucial for the advancement of collaborative machine learning in many domains such as healthcare, finance, and security. Sharing through secure computation and other means allow these partners to perform privacy-preserving computations on their private data in controlled ways. However, in reality, there exist complex relationships among members (partners). Politics, regulations, interest, trust, data demands and needs prevent members from sharing their complete data. thus, there is a need for a mechanism to meet these conflicting relationships on data sharing. this paper presents CURIE1, an approach to exchange data among members who have complex relationships. A novel policy language, CPL, that allows members to define the specifications of data exchange requirements is introduced. With CPL, members can easily assert who and what to exchange through their local policies and negotiate a global sharing agreement. the agreement is implemented in a distributed privacy-preserving model that guarantees sharing among members will comply with the policy as negotiated. the use of CURIE is validated through an example healthcare application built on recently introduced secure multi-party computation and differential privacy frameworks, and policy and performance trade-offs are explored.

关键词： Collaborative learning policy language secure data exchange

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Large Scale PoC Experiment with 57,000 people to Accumulate Patterns for Lifestyle Authentication 19

Large Scale PoC Experiment with 57,000 people to Accumulate ...

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9th acm conference on data and application security and privacy (codaspy)

作者： Kobayashi, Ryosuke Saji, Nobuyuki Shigeta, Nobuo Yamaguchi, Rie Shigetomi Mitsubishi Elect Informat Syst Corp Minato Ku 4-13-23 Shibaura Tokyo 1080023 Japan INFOCORPUS Inc Meguro Ku 3-5-1 Shimomeguro Tokyo 1530064 Japan Univ Tokyo Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

ISBN: (纸本)9781450360999

the spread of ICT has made it possible for people to use various online services via the Internet. User authentication technique is important for using online services in order to confirm that the user is legitimate. there are already some authentication methods, but several problems have been pointed out in them. We focused attention on lifestyle authentication as a new individual authentication method, that utilizes human behavior information. then, we conducted a large scale PoC experiment in order to collect human behavior information, and we succeeded in gathering data of about 57,000 people. By analyzing the collected data, we found that human behavior is patterned. In this paper, we introduce the large scale PoC experiment and the analysis result of human behavior.

关键词： Lifestyle Authentication Human Behavior IoT Smartphone

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