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

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Research on privacy protection scheme based on data fuzzy processing 23

Research on privacy protection scheme based on data fuzzy pr...

引用

proceedings of the 2023 5th International conference on Internet of things, Automation and Artificial Intelligence

作者： Yewen Fu Yongzheng Li Zhexue Jin Network information Security Laboratory Yanbian University Engineering College China

ISBN: (纸本)9798400716485

the continuous development of cloud storage has brought unprecedented convenience to people's lives. However, along with the convenience, it also brings certain security risks. Cloud data can be maliciously used, modified, leaked, and spread. the presence of sensitive information in publicly available files can pose security risks and be exploited for malicious purposes. In response to these issues, this paper proposes a scheme for fuzzy processing of public data. It introduces the Trie tree method to identify sensitive keywords in the text, uses weighted entropy to enhance classification reliability, and utilizes SHA-256 and XOR to achieve unique desensitization replacement for the keywords in the text. through experiments, the reliability of this scheme, the efficiency of encrypting and decrypting sensitive characters in the text, and the security of user data are demonstrated.

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A Review of data-Driven Approaches with Emphasis on Machine Learning Base Intrusion Detection Algorithms 5

A Review of Data-Driven Approaches with Emphasis on Machine ...

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5th International conference on Information Technology for Education and Development, ITED 2022

作者： Musa, Maryam Abdullahi Gital, Abdulsalam Ya'U Ibrahim, Kabiru Musa Chiroma, Haruna Abdulrahman, Mustapha Lawal Umar, Ibrahim Muhammad Abubakar Tafawa Balewa University Department of Mathematical Science Bauchi Nigeria Abubakar Tafawa Balewa University Department of Management Information Technology Bauchi Nigeria Department University of Hafr Al Batin Saudi Arabia

ISBN: (纸本)9781509064229

the importance of the internet across the globe cannot be over-emphasized as such network security is essential to curb future attack occurrences. Cyber-attacks like DDoS and Ransomware yielded a lot of damage to connected devices by endangering and accessing them, notwithstanding these damages are air marked to be on the rise. To overcome these issues, machine learning has been used in different computing aspects such as cyber-Intrusion Detection. Recently, deep learning, extreme learning, and deep extreme learning networks have superseded machine learning in this context due to their iterative hidden layers that can manipulate complex features of cyber intrusion data. Hence, this research surveys the application of data-driven intelligent algorithms for cyber security attack detection in comparison to conventional machine learning techniques. the review focuses on the performance evaluation of several state-of-the-art intelligent algorithms and provides research gaps and future 2trends in the context of data security Attacks and Cyber Intrusion Detection. © 2022 IEEE.

关键词： Intrusion detection

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Research on data Protection of Electric Power Marketing Based on Differential privacy

Research on Data Protection of Electric Power Marketing Base...

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Machine Learning and Computer application (ICMLCA), International conference on

作者： Yimin Wang Lei Zhao Ziyue Yang Yulong Ma Haoyou Han Lingxuan Tian Congdong Lv Jiangsu Electric Power co. LTD State Grid Corporation of China Nanjing China Nanjing Yishu Information Technology co. LTD Nanjing China Nanjing Audit University Nanjing China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

In this paper, we proposed an improved electricity marketing data protection algorithm based on differential privacy, aiming to address several challenges faced by traditional differential privacy mechanisms in practical applications. Our solution introduces a dynamic privacy budget allocation mechanism, which can intelligently adjust the strength of privacy protection based on query characteristics, achieving a balance between data utility and privacy protection. In addition, we have designed a multi-layer privacy protection architecture that combines technologies such as homomorphic encryption and federated learning to construct a comprehensive data protection system, improving the flexibility and security of data processing. the algorithm also supports personalized privacy preference settings for users, optimizing noise addition strategies through deep learning technology to achieve more precise data utility protection. Meanwhile, we have developed real-time privacy impact assessment and verifiable privacy proof capabilities, increasing the transparency and credibility of the system. Finally, in order to address the privacy protection needs of cross domain data fusion, we propose a cross domain privacy coordination mechanism to ensure privacy consistency of data in different scenarios. the experimental results show that the algorithm significantly improves the availability and utility of data while ensuring user privacy, providing a more reliable and efficient data protection solution for power marketing data analysis.

关键词： privacy Differential privacy Machine learning algorithms Heuristic algorithms Electricity Noise data protection Dynamic scheduling Resource management Protection

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application of Edge Computing in Investigation and Interrogation 21

Application of Edge Computing in Investigation and Interroga...

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5th International conference on Electronic Information Technology and Computer Engineering, EITCE 2021

作者： Gong, Siyuan Chen, Xiang People's Public Security University of China Beijing China

ISBN: (纸本)9781450384322

Under the background of the trial-centered litigation system reform, the investigation and interrogation work, as the basic link before trial in criminal proceedings, has required higher standards of evidence collection procedures. the characteristics of edge computing, such as low delay transmission, reliable sharing, data optimization and data security management, as well as its supporting moving object detection technology, video frame diversion technology and intelligent monitoring system, make it highly adaptable to the application in the field of investigation and interrogation. At the same time, the algorithm and model carried by edge computing can well calculate and control the evidence collection process. Moreover, the special method of processing video data of investigation and interrogation procedures also reduces space of data storage and the cost of examination, thus continuously advancing the trial-centered litigation system reform. © 2021 acm.

关键词： Edge computing

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Ensuring security of encrypted information by hybrid AES and RSA algorithm with third-party confirmation 5

Ensuring security of encrypted information by hybrid AES and...

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5th International conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Al Mamun, S. Mahmood, Md. Ashiq Amin, Md Ashiqul Institute of Information and Communication Technology Khulna University of Engineering Technology Khulna Bangladesh School of Informatics Computing and Cyber System Northern Arizona University AZ United States

ISBN: (纸本)9781665412728

Information transmission security and wellbeing is an intriguing issue right now. secure information transmission alludes to the exchange of information, for example, private or exclusive data over a safe channel. Many secure transmission techniques require any encryption. As of late system, security has become a significant issue. Encryption has come up as an answer and assumes a significant job in the data security framework. Numerous procedures are expected to secure mutual information. Different encryption calculations are utilized for the security and wellbeing of information transmission. the principle objective is to manage the structure of any encryption calculation against unapproved assaults. Outsider check is utilized to expand the security of every single pragmatic application and execution. this paper introduces estimation and ensuring security of encrypted information by hybrid AES and RSA algorithm with third-party confirmation. this is a secured authentication system that supports both the privacy of messages and the authenticity of communicating parties is proposed. the key goal guiding the encryption algorithm's design must be a defense against unauthorized attacks. this paper proposes an encryption algorithm to address security and privacy to protect the data from unauthorized persons. © 2021 IEEE.

关键词： Authentication

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LEChain:Linear Credit Evaluation System Based on Hyperledger Fabric Blockchain 22

LEChain:Linear Credit Evaluation System Based on Hyperledger...

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proceedings of the 2022 5th International conference on Blockchain Technology and applications

作者： Yuncheng Qiao Linhai Cheng Qiujun Lan Yiran Wang Business School Hunan University China

ISBN: (纸本)9781450397575

data sharing among different institutions can enhance the integrity of information to evaluate the credit level of financing enterprises more accurately, but it also faces the problems of privacy leakage and data fraud. To solve these problems, this paper proposes a complete linear credit evaluation system with secure sharing and multiparty collaborative computing based on Hyperledger Fabric Blockchain named LEChain. the system consists of four modules: distributed ledger, access control, security aggregation and model storage. With the help of 1-out-of-N oblivious transmission protocol, incorporating elementary matrix transformation data security aggregation realizes the privacy of data transmission and collaborative computation. Finally, a security analysis is given to show the privacy and compliance of the data aggregation.

关键词： Credit evaluation security aggregation Hyperledger fabric privacy preservation

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Fingerprint Presentation Attack Detection Algorithm 5

Fingerprint Presentation Attack Detection Algorithm

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5th National conference of Saudi Computers Colleges, NCCC 2022

作者： Alsubhi, Alaa Alsufyani, Nawal Cyber Security Computers and Information Technology Taif University Taif Saudi Arabia

ISBN: (纸本)9798350333688

Given the growing interest in security systems, researchers and developers are continually devising new methods of using biometrics, in particular fingerprints. However, hackers have discovered a flaw in the fingerprint method and exploit it through presentation attacks (PA). As a result, there is a need to protect the safety and security of authentication systems against PA through automatic presentation attack detection (PAD). the current research showed promising results after the application of PAD to deep learning (DL) algorithms. the proposed model is a softwarebased approach, namely a binary classification system to distinguish between live and fake fingerprint images as these were the most appropriate in terms of cost. In order to achieve the goal of this research, we applied a three-layer convoluional neural network (CNN) using the ATVS-FFp DB dataset for training and testing. Two different splits of the data have been tested in this worksthe first time, the proportions were training =60%, valid =20%, and testing =20%, and in the second division, we reduced the images for verification, increased them in the test, and kept the training rate as it was, namely the valid =10%, and testing =30%. the model showed that it is affected by the number of images in the test and shows higher results the more the test phase is given. the trained model achieved 99% training accuracy and 97% testing accuracy. © 2022 IEEE.

关键词： Biometrics

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Formal Analysis of ReBAC Policy Mining Feasibility 21

Formal Analysis of ReBAC Policy Mining Feasibility

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11th acm conference on data and application security and privacy (CODASPY)

作者： Chakraborty, Shuvra Sandhu, Ravi Univ Texas San Antonio Inst Cyber Secur ICS San Antonio TX 78249 USA Univ Texas San Antonio NSF Ctr Secur & Privacy Enhanced Cloud Comp C SPE Dept Comp Sci San Antonio TX 78249 USA

ISBN: (纸本)9781450381437

Relationship-Based Access Control (ReBAC) expresses authorization in terms of various direct and indirect relationships amongst entities, most commonly between users. the need for ReBAC policy mining arises when an existing access control system is reformulated in ReBAC. this paper considers the feasibility of ReBAC policy mining in context of user to user authorization, such as arises in various social and business contexts. In accordance with the policy mining literature, we assume that complete data is provided regarding user to user authorizations for a given user set, along with complete relationship data amongst these users comprising a labeled relationship graph. A ReBAC policy language is also specified. ReBAC policy mining seeks to formulate a ReBAC policy with the given policy language and relationship graph, which is exactly equivalent to the given authorizations. ReBAC policy mining feasibility problem asks whether such a policy exists and if so to provide the policy. We investigate this problem in context of different ReBAC policy languages which differ in the relationships, inverse relationships and non-relationships that can be used to build the policy. We develop a feasibility detection algorithm and analyze its complexity. We show that our policy languages are progressively more expressive as we introduce additional capability. In case of infeasibility, various solution approaches are discussed.

关键词： Access control Policy mining Relationship-Based Access Control

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Anomaly Detection in IoT Networks: Leveraging Federated Learning for Real-Time threat Response

Anomaly Detection in IoT Networks: Leveraging Federated Lear...

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Global conference for Advancement in Technology (GCAT)

作者： Shiva Mehta Sunila Choudhary Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

the impressive growth of the Internet of things has produced considerable security challenges because of the heterogeneity of different devices and data volumes. this research article introduces an elaborate Internet of things (IoT) security architecture which employs Federated Learning (FL) to address such challenges. the framework enhances IoT security by integrating federated learning with enhanced anomaly detection, better privacy-preserving mechanisms, and robust secured communication technology. It also pays special attention to preventing data exposure and minimizing the amount of communication done. the proposed architecture was evaluated in a synthesized IoT environment to put it against centralized security approaches. the one implemented in Florida also reached a detection accuracy of 93.7 %, in contrast to the previous report of 85.3% accuracy realised with centralised approaches. In addition, the framework was demonstrated to be highly efficient, especially in environments that lack resources where communication overhead was reduced by 250 MB to 180 MB. the application of privacy-preserving methodologies allowed us to score 9 on a scale of 10, whereas the regular integration of the approach has a score of 6. this underlines the increased protection of the details and information. the study shows that Federated Learning has the potential to significantly boost the security of IoT networks by offering a secure and privacy-preserving solution. the proposed framework is superior to the current methods in terms of precision and time and also in terms of data privacy. this work offers a good ground for the development and integration of FL-based security approaches in other IoT scenarios.

关键词： privacy Accuracy Protocols Federated learning Real-time systems Internet of things security Protection Homomorphic encryption Anomaly detection

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An Intelligent Doorbell Design Using Federated Deep Learning 21

An Intelligent Doorbell Design Using Federated Deep Learning

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3rd acm India Joint International conference on data Science and Management of data (CODS-COMAD)

作者： Patel, Vatsal Kanani, Sarth Pathak, Tapan Patel, Pankesh Ali, Muhammad Intizar Breslin, John Pandit Deendayal Petr Univ Gandhinagar Gujarat India NUI Galway Data Sci Inst Confirm SFI Res Ctr Smart Mfg Galway Ireland

ISBN: (纸本)9781450388177

Smart doorbells have been playing an important role in protecting our modern homes. Existing approaches of sending video streams to a centralized server (or Cloud) for video analytics have been facing many challenges such as latency, bandwidth cost and more importantly users' privacy concerns. To address these challenges, this paper showcases the ability of an intelligent smart doorbell based on Federated Deep Learning, which can deploy and manage video analytics applications such as a smart doorbell across Edge and Cloud resources. this platform can scale, work with multiple devices, seamlessly manage online orchestration of the application components. the proposed framework is implemented using state-of-the-art technology. We implement the Federated Server using the Flask framework, containerized using Nginx and Gunicorn, which is deployed on AWS EC2 and AWS Serverless architecture.

关键词： Federated Learning Internet of things Video Analytics Artificial Intelligence Deep Learning Machine Learning privacy security

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