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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"

共 1519 条记录，以下是111-120 订阅

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Federated Learning Approaches for Decentralized data Processing in Edge Computing 5

Federated Learning Approaches for Decentralized Data Process...

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5th International conference on Smart Electronics and Communication, ICOSEC 2024

作者： Janaki, G. Umanandhini, D. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science & Engineering Chennai India

ISBN: (纸本)9798331504403

Federated Learning (FL) has evolved into a decentralized Machine Learning (ML) paradigm with a high potential for enabling model training across various Edge Devices (ED) to conserve data privacy. this survey explores the challenges and future directions of implementing FL in edge computing environments. this Comprehensive review addresses key technical challenges, including the heterogeneity of edge devices, resource constraints, security and privacy concerns, and communication overhead. To guide future research, this study highlights several promising research directions, including advanced model aggregation techniques, edge device optimization, cross-domain federated learning applications, robust security and privacy frameworks, and regulatory compliance. Additionally, this study reviews various Federated Learning methods-such as Federated Averaging (FedAvg), Federated Learning with Secure Aggregation (FedSecAgg), Hierarchical Federated Learning, and Peer-to-Peer Federated Learning-discussing their strengths, weaknesses, and suitable use cases. Finally, this review paper presents benchmarking results using standard datasets and performance metrics, comparing these approaches' efficiency, accuracy, scalability, and privacy preservation. this systematic review renders a broad perspective on the current trends and developments in federated Learning for edge computing, this analysis enlightenments researchers and Professionals with practical experience. © 2024 IEEE.

关键词： Federated learning

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An Investigation into Misuse of Java security APIs by Large Language Models 19

An Investigation into Misuse of Java Security APIs by Large ...

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19th acm Asia conference on Computer and Communications security (acm AsiaCCS)

作者： Mousavi, Zahra Islam, Chadni Moore, Kristen Abuadbba, Alsharif Babar, Muhammad Ali Univ Adelaide CREST Adelaide SA Australia CSIRO Data61 Cybersecur CRC Canberra ACT Australia Queensland Univ Technol Brisbane Qld Australia CSIRO Data61 Canberra ACT Australia

ISBN: (纸本)9798400704826

the increasing trend of using Large Language Models (LLMs) for code generation raises the question of their capability to generate trustworthy code. While many researchers are exploring the utility of code generation for uncovering software vulnerabilities, one crucial but often overlooked aspect is the security application Programming Interfaces (APIs). APIs play an integral role in upholding software security, yet effectively integrating security APIs presents substantial challenges. this leads to inadvertent misuse by developers, thereby exposing software to vulnerabilities. To overcome these challenges, developers may seek assistance from LLMs. In this paper, we systematically assess ChatGPT's trustworthiness in code generation for security API use cases in Java. To conduct a thorough evaluation, we compile an extensive collection of 48 programming tasks for 5 widely used security APIs. We employ both automated and manual approaches to effectively detect security API misuse in the code generated by ChatGPT for these tasks. Our findings are concerning: around 70% of the code instances across 30 attempts per task contain security API misuse, with 20 distinct misuse types identified. Moreover, for roughly half of the tasks, this rate reaches 100%, indicating that there is a long way to go before developers can rely on ChatGPT to securely implement security API code.

关键词： security API Misuse ChatGPT LLM-generated Code Software security Secure Software Development

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A Survey on the application of Blockchain Technology for Cyber-Physical Systems 5

A Survey on the Application of Blockchain Technology for Cyb...

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5th International conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Nelufule, Norman Senamela, Pertunia Shadung, Daniel Singano, Tanita Masemola, Kelebogile Mangole, Tshegofatso Pretoria South Africa

ISBN: (纸本)9798350389609

this paper presents a systematic review of the literature on the application of blockchain technology to improve the security of cyber-physical systems. the objective of the article was to identify current challenges, evaluate existing solutions, and propose future research directions. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to ensure that the result of this comprehensive review is not biased. the results and key findings have highlighted that there is a potential usage of the blockchain technology to address security challenges in a cyber-physical systems, including data integrity, authentication, and secure communication. this survey paper concludes by presenting the recommendations for integrating blockchain technology into CPSs to enhance their security and resilience. © 2024 IEEE.

关键词： Cybersecurity

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Anonymization of Sensitive data in the Diabetes Prediction using Private Bayesian Networks 5

Anonymization of Sensitive Data in the Diabetes Prediction u...

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5th International conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Sethukarasi, T. therasa, P.R. thilagavathy, A. Ajay Kumar, T.M. Dinesh, P.N. Devi Prasad, S.M. R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Thiruvallur India

ISBN: (纸本)9798350367171

In the modern era of automated learning and computational intelligence, early disease detection is critical for assisting practitioners in disease diagnosis and decision-making. Traditional methods, including Support Vector Machine (SVM), Random Forest (RF), and XGBoost, have proven effective for disease predictions but raise significant privacy concerns due to the sensitive nature of the data involved. To mitigate these privacy issues, this research study proposes the creation of a synthetic dataset for training the supervised classification models for Diabetes Mellitus (DM) prediction, ensuring the preservation of the ground truth. the proposed method employs a discriminative-generative distillation approach using private Bayesian networks to protect the data confidentiality. By using the models as intermediaries to extract information from personal data, the proposed technique enables knowledge transfer to a student network through dual channels, preventing data leakage and safeguarding sensitive healthcare information. this approach enhances the security and privacy of medical data while maintaining the accuracy and reliability of predictive models. © 2024 IEEE.

关键词： Information leakage

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A Comprehensive Analysis on Blockchain Based Healthcare data security 5

A Comprehensive Analysis on Blockchain Based Healthcare Data...

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5th International conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Selvanayagi, A. Ramya, K. Shivani, S. Suwathika, K. M.Kumarasamy College of Engineering Computer Science and Engineering Karur639113 India

ISBN: (纸本)9798350385649

Blockchain generation has the capability to revolutionize the management and sharing of Electronic Medical Records (EMRs). By using a distributed ledger, blockchain can set up a steady document of transactions, making it a suitable solution for safeguarding the data from unauthorized data access and modification. One technique to leverage block chain for EHRs involves the utilization of SHA-256 algorithm. this cryptographic hash function allows the era of a unique identifier, known as a hash, for each information detail. When a brand new EHR is generated, its hash is calculated and saved at the blockchain, thereby setting up a steady and tamper-evidence record of the EHR. In the event that a EHR undergoes any unauthorized changes, its hash will trade accordingly. Consequently, any unauthorized alterations to the EHRmay be detected immediately. the emergence of the COVID-19 pandemic has underscored the critical need for privacy protection of individuals who test positive for the virus. However, the existing tracing systems face challenges such as privacy violations, potential leakage of sensitive information, and susceptibility to cyberattacks. this feature of block chain makes it an ultimate technique for making sure the integrity of EHRs. © 2024 IEEE.

关键词： Blockchain

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Evolution of Big data Adoption in Industries Using TOPSIS Method 5

Evolution of Big Data Adoption in Industries Using TOPSIS Me...

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

作者： Sharma, Himanshu Singh, Juginder Pal Gore, Santosh Sathawane, Neerajkumar Golhar, Roshni S. Swami, Vishwajeet Madhav GLA University Department of Computer Engineering & Applications Mathura India Sai Info Solution Maharashtra Nashik India Rajarshi Shahu College of Engineering MH Pune India Suman Ramesh Tulsiani Technical Campus Kamshet Pune India Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research Pimpri Pune411018 India

ISBN: (纸本)9798331530013

Big data Adoption represents an essential method for corporations aiming to enhance productivity and manage patron demands extra effectively. Despite its developing significance, there remains a lack of comprehensive reviews and classifications of current research on this subject matter. Industry leaders have found that digitalization, driven through declining technological charges, has made Big data an outstanding focus in information technology and finance. Recent facts highlight a global growth in Big data investments, signaling its growing importance inside the enterprise area. However, the route to a successful adoption is complex and requires time, notwithstanding the promise of multiplied velocity and efficiency. Big data refers to great and complex datasets that conventional data control equipment war to address. Its price lies not simply in its quantity or velocity but in its reliability and effectiveness for processing and evaluation. Key elements influencing Big data adoption consist of compatibility, complexity, perceived advantages, technological assets, trialability, privacy and protection, and records fine. the TOPSIS method is identified as an effective device for evaluating Big data solutions, as it allows for evaluating alternatives primarily based on distance metrics. Findings indicate that security and privacy are paramount issues, whilst facts satisfaction and integration are less important but still vital. As Big data continues to conform, agencies ought to navigate those elements to leverage their full ability and gain strategic advantages. © 2024 IEEE.

关键词： Decentralized finance

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Verifiable Results privacy Protection Federal Learning Model Based on the Blockchain 24

Verifiable Results Privacy Protection Federal Learning Model...

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5th International conference on Computer Information and Big data applications, CIBDA 2024

作者： Qian, Yuhua Hu, Tan Han, Nian School of Chongqing University of Technology ChongQing China

ISBN: (纸本)9798400718106

Federated learning is a decentralized learning method used to train machine learning algorithms. In FL, the global model iteratively collects the parameters of local models without directly accessing the local data of local parties. However, traditional federated learning relies on central servers, and the model training process is vulnerable to single points of failure and malicious node attacks. Intermediate parameters passed in plain text can also be used to infer private information in the data. To solve the above problems, current solutions adopt different strategies, such as differential privacy computation, secure multi-party computation, multi-server aggregation, adding noise masking and other privacy protection methods, but these methods may affect the accuracy and precision of the model. the communication cost during the training process is high, making it difficult to apply the above solution in practice. therefore, this paper proposes a blockchain-based privacy-preserving federated learning model with verifiable results. Use blockchain to replace traditional parameter servers to achieve decentralization and traceability of the upload process. Use optimized homomorphic encryption technology to protect the privacy of intermediate parameters of collaborative training parties and reduce communication costs;use BLS aggregate signature technology to verify local models. the integrity of parameters enables secure management of keys through a secret sharing scheme. Simulation experiments prove that the training accuracy of the model on the MNIST data set is basically consistent with traditional centralized machine learning. It can ensure that the model is trained in a decentralized manner in a multi-party collaboration environment, effectively protecting the privacy and security of all participants. © 2024 acm.

关键词： Blockchain

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k-Anonymity in Federated Heterogenous Graphs and k-Core Anonymization 5

k-Anonymity in Federated Heterogenous Graphs and k-Core Anon...

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5th IEEE International conference on Trust, privacy and security in Intelligent Systems and applications (TPS-ISA)

作者： Dockendorf, Mark Dantu, Ram Univ North Texas Dept Comp Sci & Engn 1155 Union Cir Denton TX 76203 USA

ISBN: (纸本)9798350323856

k-Anonymity is widely used to preserve privacy of individuals by ensuring at least k data points are indistinguishable from one-another. In this paper, we apply k-anonymity to our federated heterogeneous graph storage technique, category cluster. this application of k-anonymity allows select graph edges that would otherwise have to be encrypted with homomorphic encryption to remain in cleartext. We also demonstrate k-core anonymization, a graph anonymization technique based on k-core decomposition. We benchmark anonymized versions of Youtube and LiveJournal social networks against their original counterparts. this anonymization method preserves core membership well (less than 0.7% induced error). k-core anonymization also preserves the distribution of longest paths;however, our results show this distribution is shifted in phase from the original.

关键词： k-Anonymity Graphs data Cooperatives Anonymization Homomorphic Encryption

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LLMEdge: A Novel Framework for Localized LLM Inferencing at Resource Constrained Edge 5

LLMEdge: A Novel Framework for Localized LLM Inferencing at ...

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

作者： Ray, Partha Pratim Pradhan, Mohan Pratap Sikkim University Department of Computer Applications Gangtok737102 India

ISBN: (纸本)9798331518097

Large Language Models (LLMs) have revolutionized natural language processing, providing robust capabilities for understanding and generating human readable text. However, deploying these models in resource-constrained edge environments, such as Internet of things (IoT) platforms, remains a significant challenge due to the high computational and memory requirements of LLMs. this paper introduces LLMEdge, a novel framework proposed to address these challenges by leveraging quantized LLMs, efficient localized inference frameworks, and lightweight web application servers. Our contributions include quantization-aware LLM deployment for near real-time inference on resource-constrained edge devices, reducing cloud dependency, minimizing power consumption, enhancing user experience, and improving data privacy. LLMEdge holds promise for scalable, low-latency, and privacy-focused solutions in diverse IoT applications, paving the way for more intelligent and autonomous edge systems. © 2024 IEEE.

关键词： data privacy

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data security and privacy Protection of Technology Achievement Transformation Platform in Vocational Colleges Based on Blockchain 5

Data Security and Privacy Protection of Technology Achieveme...

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5th International conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2024

作者： Song, Yang Jilin Communications Polytechnic Changchun Jilin130012 China

ISBN: (纸本)9798331539818

In recent years, the role of the transformation of scientific and technological achievements in vocational colleges in promoting regional innovation and economic development has become increasingly significant. However, ensuring the security and privacy of data in technology transfer platforms remains a key challenge. Differential privacy, as a privacy protection strategy, has been applied in many fields in recent years. It can protect individuals' privacy information without compromising data features. this article proposes a new method to address these issues by integrating differential privacy mechanisms into blockchain technology to protect data privacy. In response to the issue of shared data privacy protection in blockchain networks, a blockchain data privacy protection scheme based on differential privacy is proposed. By integrating differential privacy mechanisms into the blockchain smart contract layer, the process automatically invokes chain code to add noise to the data uploaded by users. Noise data is then called using permission control on the Hyperledger Fabric private ledger, separating the original data from the noise data. Random noise that conforms to the differential privacy policy is added separately to different types of data and stored in the private ledger. through the noise data stored in the private ledger, data can be denoised to respond to users' data verification requests, ultimately achieving consensus status in the blockchain network. Finally, the privacy protection performance of the system is measured by evaluating the throughput of the blockchain network and the consumption of privacy. © 2024 IEEE.

关键词： Smart contract

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