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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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DIPA: An Image dataset with Cross-cultural privacy Concern Annotations 28

DIPA: An Image Dataset with Cross-cultural Privacy Concern A...

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28th Annual conference on Intelligent User Interfaces (IUI)

作者： Xu, Anran Zhou, Zhongyi Miyazaki, Kakeru Yoshikawa, Ryo Hosio, Simo Yatani, Koji Univ Tokyo Interact Intelligent Syst Lab Tokyo Japan Univ Oulu Ctr Ubiquitous Comp Oulu Finland

ISBN: (纸本)9798400701078

Image privacy protection is an important topic in Human-Computer Interaction and usable security. Researchers have examined different aspects of image privacy by collecting samples by themselves. However, there does not exist a publicly-available dataset on image privacy, which prevents these efforts from sharing common technical foundations. We introduce DIPA, an open source dataset that provides content-level annotations that specifically focus on image privacy. We include 1,495 images from two existing datasets in DIPA, and augment them with 5,671 annotations. Each annotation includes reasons why the associated visual content can be privacy-threatening, a rating of how informative annotators thought the associated content is to threaten privacy, and another rating of how broadly the image could be shared. We also collected annotations from people living in Japan and UK to enable researchers and developers to perform analysis from the perspective of cultural differences. In this paper, we present the construction procedure of DIPA and report high-level statistics of the data we obtained. We hope that DIPA would accelerate various future research, including quantitative understandings of cultural differences on perceptions of image privacy and the development of robust recognition models for image privacy protection.

关键词： Image privacy usable security

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Integrating Artificial Open Generative Artificial Intelligence into Software Supply Chain security 5

Integrating Artificial Open Generative Artificial Intelligen...

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5th International conference on data Analytics for Business and Industry, ICDABI 2024

作者： Alevizos, Vasileios Papakostas, George A. Simasiku, Akebu Malliarou, Dimitra Messinis, Antonis Edralin, Sabrina Xu, Clark Yue, Zongliang Karolinska Institutet Solna Sweden Democritus University of Thrace MLV Research Group Department of Informatics Kavala Greece Zambia University Ndola Zambia IntelliSolutions Athens Greece HEDNO SA Athens Greece University of Illinois Urbana-Champaign IL United States Mayo Clinic Artificial Intelligence and Discovery MN United States Auburn University Harrison College of Pharmacy AL United States

ISBN: (纸本)9798350368710

While new technologies emerge, human errors always looming. Software supply chain is increasingly complex and intertwined, the security of a service has become paramount to ensuring the integrity of products, safeguarding data privacy, and maintaining operational continuity. In this work, we conducted experiments on the promising open Large Language Models (LLMs) into two main software security challenges: source code language errors and deprecated code, with a focus on their potential to replace conventional static and dynamic security scanners that rely on predefined rules and patterns. Our findings suggest that while LLMs present some unexpected results, they also encounter significant limitations, particularly in memory complexity and the management of new and unfamiliar data patterns. Despite these challenges, the proactive application of LLMs, coupled with extensive security databases and continuous updates, holds the potential to fortify Software Supply Chain (SSC) processes against emerging threats. © 2024 IEEE.

关键词： Large Language Models Software Supply Chain security Vulnerabilities

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Malware Detection Using Machine Learning Algorithms in Android 5th

Malware Detection Using Machine Learning Algorithms in Andro...

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5th International conference on data Science, Machine Learning and applications

作者： Sri, Kovvuri Ramya G Narayanamma Inst Technol & Sci Hyderabad 500104 India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Malware remains a critical challenge within the domain of working frameworks and program, with Android frameworks being no special case. In spite of past endeavors utilizing Signature-based methods for malware detection, their restrictions in distinguishing obscure malwares are apparent. the scene is characterized by a large number of location and investigation strategies, however viably tending to the precise recognizable proof of novel malware remains a basic concern. Our technique involves leveraging a comprehensive dataset of consents related with pernicious applications. Furthermore, our approach amplifies past authorizations and dives into the semantic layer by subjecting the application's comments to thorough investigation. through this comprehensive system, we point to supply an inventive and compelling arrangement that increases the exactness of malware location, especially centering on tending to the challenges postured by rising and already obscure malware strains.

关键词： Malware Detection Android security

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Secured data data Movement Movement using using data data Ring Ring Fencing Fencing 5

Secured Data Data Movement Movement using using Data Data Ri...

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5th IEEE International conference on Trust, privacy and security in Intelligent Systems and applications, TPS-ISA 2023

作者： Nangia, Aditya Bhupal, Saksham Mohania, Mukesh Kundu, Chinmay Iiit Delhi India Kiit University Bhubaneswar India

ISBN: (纸本)9798350323856

the exponential increase in data volume, the growing pervasiveness of digital technologies, and frequent data breaches have given rise to a broad range of privacy concerns. these concerns stem from the fact that data privacy measures have not kept pace with the scale and heterogeneity of sensitive data being produced, leading to unauthorized and unintended access, especially in multi-party scenarios. In the context of enterprises and individuals, data is one of the most important crown jewels to be protected. Current data protection frameworks struggle to regulate data sharing across multiple parties due to the existence of varying data access rules, and privacy and data governance policies across these institutions and individuals. the notion of Ring Fencing has been used in the financial sector to create a virtual barrier that isolates a portion of a company's financial assets to mitigate risk. In this paper, we propose to leverage this concept in order to enhance data security through the integration of a data Ring Fencing architecture. Our data Ring Fencing framework offers a multi-layered security paradigm that models privacy-preserving data-sharing agreements across multiple institutions. It achieves this goal by governing data privileges and regulating who can access what and for what purpose, and at what cost. Our data ring fencing framework supports three phases: Evaluate, Enforce, and Execute (referred to as the three-E architecture). the three-E architecture leverages deep learning-based models such as BERT + Multiclass LR for query classification and Transfer Learning on a sequence-to-sequence model for text-to SQL generation. We demonstrate a practical application of the three-E architecture based on data Ring Fencing using a multi-institution instance from the healthcare domain. After conducting extensive experimentation on our custom datasets, our BERT + Multiclass LR model gave us the best performance achieving more than 99% accuracy. © 2023 IEEE.

关键词： Sensitive data

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A Framework for Securing Electronic Payments via the Dual Channel security Protocol 5

A Framework for Securing Electronic Payments via the Dual Ch...

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5th IEEE Middle East and North Africa COMMunications conference, MENACOMM 2025

作者： Barada, Maria Mshellem, Afnan Shaker, May Mershad, Khaleel Cheikhrouhou, Omar Department of Computer Science and Mathematics Beirut Lebanon Higher Institute of Computer Science of Mahdia University of Monastir Mahdia Tunisia

ISBN: (纸本)9798331519957

A major concern in e-payment systems is the privacy of user data. Recently, marketplaces have been utilizing point-of-sale terminals that use short-range communication for contactless payments. Such services primarily rely on the NearField Communication protocol. Previous works showed that this protocol is susceptible to many attacks such as eavesdropping, data corruption, and Man-in-the-middle. Dual Channel security, or DCS, is one of the simplest and most efficient solutions that address the weaknesses inherent in protocols that employ an optical key agreement link. the main objective of DCS is to protect short-range communications against wireless attacks. In this paper, we describe a novel implementation of the DCS protocol and evaluate its effectiveness within a concrete ecommerce application. We utilize several algorithms to develop the proposed e-payment service. In addition, we describe the construction of the key exchanged between the devices using DCS, the use of P2P communication, and the *** technology that ensures data exchange between these devices. © 2025 IEEE.

关键词： Near field communication

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Hybrid Cloud Workflow Scheduling Algorithm Based on the Improved Wild Horse Optimization Algorithm 5

Hybrid Cloud Workflow Scheduling Algorithm Based on the Impr...

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5th International conference on Computer Engineering and application (ICCEA)

作者： Chen, Xiaobo Qiu, Lupeng Li, Tianzhe Fan, Yingkai Zhou, Naqin Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

As a cloud computing model that integrates the advantages of public and private clouds, the hybrid cloud adapts to changing application requirements, allowing users to flexibly lease public cloud resources to expand computing capabilities, effectively alleviating the problem of resource scarcity in private clouds during peak periods. However, existing hybrid cloud workflow scheduling inadequately considers the data privacy and security risks associated with cross-platform data transmission while meeting deadlines and optimizing costs. therefore, this paper proposes a hybrid cloud workflow scheduling algorithm (HCWSA-IWHO) based on an improved wild horse optimization algorithm. the algorithm combines intelligent optimization algorithms and heuristic algorithms, divided into pre-scheduling and dynamic scheduling stages. In the pre-scheduling stage, by using an adaptive pairing range and selection method for pairing objects, the algorithm ensures the diversity of initial population individuals and the convergence of search space in later stages. In the dynamic scheduling stage, tasks are classified, and tasks that need to be reallocated in the public cloud are identified. When reallocating virtual machines for each task, virtual machines that meet performance requirements and minimize costs are selected to achieve cost minimization. the HCWSA-IWHO algorithm is compared with the PSSA and FWAPS-VI algorithms. Experimental results demonstrate that the HCWSA-IWHO not only meets deadline requirements but also outperforms PSSA and FWAPS-VI in reducing workflow execution costs.

关键词： cloud computing hybrid cloud task scheduling data security scientific workflow

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privacy-Preserving Swarm Learning Based on Lightweight Homomorphic Encryption and Blockchain Technology 5

Privacy-Preserving Swarm Learning Based on Lightweight Homom...

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5th IEEE Eurasia conference on IOT, Communication and Engineering, ECICE 2023

作者： Li, Yong Ling, Haichao Ren, Xianglin Yu, Chun Liu, Tongtong Changchun University of Technology School of Computer Science Changchun China Non-commissioned Officer School of Army Academy of Armored Forces Changchun China

ISBN: (纸本)9798350314694

A decentralized solution called swarm learning integrates blockchain into machine learning with distributed data sources and computing power. the security of blockchain is inherited and the security and privacy of data is enhanced surpassing federated learning. Previous studies have explored the application of homomorphic encryption to privacy protection in distributed machine learning. However, existing homomorphic encryption algorithms require a large amount of computation, making them unsuitable for swarm learning. To solve this problem, we improved a privacy-preserving swarm learning framework based on an improved lightweight Paillier homomorphic encryption algorithm. We evaluated the performance of the improved algorithm under the federated learning environment and the security and the swarm environment. Experiment results showed that under the MNIST data set, the encryption time of the classical homomorphic encryption algorithm was 2.9 times longer than that of the improved algorithm. the improved algorithm improved the encryption efficiency of the homomorphic encryption algorithm and protected the privacy of swarm learning data. © 2023 IEEE.

关键词： blockchain data privacy distributed machine learning federated learning homomorphic encryption Paillier swarm learning

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Aquaculture IoT security Landscape: A Comprehensive Analysis of threats and Mitigation Strategies 5

Aquaculture IoT Security Landscape: A Comprehensive Analysis...

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

作者： Bhushan, Adithi Aimen Fathima, S. Shanthala, P.T. Pes University Computer Science Engineering India

ISBN: (纸本)9798350385649

the use of Internet of things (IoT) technologies in aquaculture has transformed the management of aquatic resources by providing formerly unknown opportunities for automation, data collection, and decision-making. However, this perspective shift also brings up substantial cybersecurity concerns that require immediate attention. this survey provides a thorough analysis of a broad range of security threats, including replay attacks, man-in-the-middle attacks, denial-of-service attacks, jamming, malicious node injection, and various other attacks that are under different layers. To improve the reliability and safety of aquaculture IoT systems, the study methodically looks into specific countermeasures at the Perception/Physical, Network, Processing, and application layers. Encryption, application-layer filtering, and physical-layer tamper-resistant technologies are recommended solutions that prioritize data integrity and security and work as an active defensive strategy. Beyond just a technological requirement, aquaculture IoT security is crucial for preserving the health of aquatic ecosystems and ensuring the long-term resilience of aquaculture operations against various security threats. this preventive strategy safeguards future developments in aquaculture IoT while highlighting the vital need for data security and quality. © 2024 IEEE.

关键词： Network security

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Federated Learning for Secure and privacy-Preserving Medical Image Analysis in Decentralized Healthcare Systems 5

Federated Learning for Secure and Privacy-Preserving Medical...

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5th International conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Muthalakshmi, M. Jeyapal, Karthik Vinoth, M. Dinesh, P.S. Murugan, N. Senthil Sheela, K.Santha Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology India Wipro India Ifet College of Engineering Autonomous Department of Chemistry Villupuram India Bannari Amman Institute of Technology Computer Science and Engineering Alathukombai Sathyamangalam India J.P.College of Engineering Department of Information Technology Tenkasi India Velammal College of Engineering and Technology Department of Cse Madurai India

ISBN: (纸本)9798350379945

the need for safe and effective ways to manage private patient information has grown in tandem with deep learning algorithms in medical image analysis. Preserving patient privacy while utilizing collective intelligence for accurate diagnostic models is difficult in decentralized healthcare systems when medical data is disseminated across many organizations and locations. this study suggests a federated learning architecture to analyze medical images in decentralized healthcare systems safely and privately. the suggested method uses federated learning concepts to enable several healthcare organizations to work together in training a deep learning model, all while keeping patient data private. Regarding data privacy and security, each institution handles things on its own. Medical image analysis can benefit from developing strong and precise deep learning models through federated learning's decentralized model training approach. the federated learning framework uses differential privacy measures, secure communication protocols, and advanced encryption techniques to guarantee privacy and security. During the training of the model, these safeguards protect sensitive medical data. the study also investigates how the privacy-preserving features of the suggested system might be improved by using federated averaging and homomorphic encryption. Various healthcare institutions' real-world medical imaging datasets are used to evaluate the federated learning system experimentally. the results show that the method can achieve centralized model performance levels while keeping data private and secure with 98.6% accuracy. Model convergence, communication overhead, and computational efficiency are other consequences of federated learning examined in the paper. © 2024 IEEE.

关键词： Decentralized Healthcare Systems Deep Learning Federated Learning Medical Image Analysis privacy-Preserving

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Experimental Evaluation of a Secured privacy Preservation Scheme using IP Traceback Logic in Wireless Sensor Networks 5

Experimental Evaluation of a Secured Privacy Preservation Sc...

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

作者： Shifani, S. Agnes Metilda, M.I. Mary Rajkumar, G. Vinoth Muppudathi Sutha, S. Maheshwari, S. Mary, A. Antony Saveetha University Saveetha School of Engineering Department of Ece Chennai India Sathyabama Institute of Science and Technology Department of Mathematics Chennai India J P College of Engineering Department of Ece Tenkasi India Sardar Raja College of Engineering Department of Ece Alangulam India Srm Institute of Science and Technology Division of Mathematics School of Sciences Trichy India

ISBN: (纸本)9798331504403

this research work proposes a secured privacy preservation scheme for wireless sensor networks (WSNs) that integrates multiple advanced techniques to ensure robust data confidentiality, integrity, and traceability. the methodology encompasses secure network initialization, symmetric key cryptography for data encryption and decryption, probabilistic packet marking for IP traceback, and secure routing protocols. Additionally, the scheme employs privacy-preserving data aggregation methods and an intrusion detection system (IDS) enhanced by machine learning for real-time threat detection and mitigation. the combination of these techniques addresses the critical security challenges in WSNs, making the model particularly suitable for sensitive applications such as military operations, healthcare, and critical infrastructure monitoring. A key feature of the proposed model is its superior performance, with encryption and decryption times of 1 ms and 0.9 ms respectively, which are significantly better than those of existing models. this comprehensive approach ensures that the WSN remains secure and reliable, even in the face of evolving cybersecurity threats. © 2024 IEEE.

关键词： Differential privacy

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