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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 Psychological Screening and Early Warning for Smart Campus based on privacy Protection 24

Research on Psychological Screening and Early Warning for Sm...

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

作者： Yu, Haiyan Zhang, Qiping Anhui Vocational and Technical College Hefei230001 China Hefei University of Technology Hefei230051 China

ISBN: (纸本)9798400718106

there are some problems of traditional campus psychological assessment such as difficult identification of backstage managers, easy leakage of psychological appointment consultation information, and untimely warning triggered by data overrun. Meanwhile, privacy protection is becoming increasingly important. this paper designs a psychological screening and early warning system for smart campus, combining psychological screening and early warning technology, asymmetric encryption, digital signature, and other technologies, by collecting on-site face images, conducting rapid emotional screening and classification, integrating in real time the psychological. the system realized the functions of face recognition, mental health classification, mental health warning, and privacy protection by collecting on-site face images, conducting rapid emotional screening and classification, and integrating the early warning situation and information of early warning personnel in real-time. the analysis results show that this scheme has high security and screening efficiency, which provides an idea for building an intelligent psychological screening and early warning security management architecture. © 2024 acm.

关键词： Classification (of information)

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Secure EEG: Secure Seizure Prediction using Encryption and Machine Learning 5

Secure EEG: Secure Seizure Prediction using Encryption and M...

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

作者： Blessy Sumam, G.G. Gayathri, S. Gopika, S. Er&dci Institute of Technology Cdac Cyber Forensics and Information Security Thiruvananthapuram India Cdac Thiruvananthapuram India Kristu Jayanti College Department of Computer Science Bengaluru India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

this research work introduces a pioneering methodology for predicting seizures by leveraging Electroencephalography (EEG) data in individuals diagnosed with epilepsy. the proposed system combines machine learning algorithms with data encryption techniques to enhance both prediction accuracy and data security. For seizure prediction, LSTM was employed due to its adaptability to varying sequence lengths and capacity for real-time processing, positioning it as a potent tool that holds promise for early prediction, enabling timely intervention and potentially enhancing overall patient well-being. To ensure data privacy, Fernet encryption is employed to encrypt model parameters and test data. the encrypted model demonstrates promising results in seizure prediction accuracy during evaluation, achieving an accuracy score of 97%. Furthermore, the integration of encryption techniques ensures the confidentiality of sensitive EEG information, meeting stringent data security standards. this novel approach presents a robust framework for accurate seizure prediction while addressing critical concerns regarding data privacy in healthcare applications. the obtained results contribute valuable insights into the model's ability to accurately discern between seizure and non-seizure states, crucial for assessing its practical utility in real-world © 2024 IEEE.

关键词： Prediction models

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Comparative privacy Analysis of Mobile Browsers 23

Comparative Privacy Analysis of Mobile Browsers

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13th acm conference on data and application security and privacy (CODASPY)

作者： Zafar, Ahsan Das, Anupam North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9798400700675

Online trackers are invasive as they track our digital footprints, many of which are sensitive in nature, and when aggregated over time, they can help infer intricate details about our lifestyles and habits. Although much research has been conducted to understand the effectiveness of existing countermeasures for the desktop platform, little is known about how mobile browsers have evolved to handle online trackers. With mobile devices now generating more web traffic than their desktop counterparts, we fill this research gap through a large-scale comparative analysis of mobile web browsers. We crawl 10K valid websites from the Tranco list on real mobile devices. Our data collection process covers both popular generic browsers (e.g., Chrome, Firefox, and Safari) as well as privacy-focused browsers (e.g., Brave, Duck Duck Go, and Firefox-Focus). We use dynamic analysis of runtime execution traces and static analysis of source codes to highlight the tracking behavior of invasive fingerprinters. We also find evidence of tailored content being served to different browsers. In particular, we note that Firefox Focus sees altered script code, whereas Brave and Duck Duck Go have highly similar content. To test the privacy protection of browsers, we measure the responses of each browser in blocking trackers and advertisers and note the strengths and weaknesses of privacy browsers. To establish ground truth, we use well-known block lists, including EasyList, Easyprivacy, Disconnect and WhoTracksMe and find that Brave generally blocks the highest number of content that should be blocked as per these lists. Focus performs better against social trackers, and Duck Duck Go restricts third-party trackers that perform email-based tracking.

关键词： Web tracking User privacy Mobile browsers

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Poster: Longitudinal Measurement of the Adoption Dynamics in Apple's privacy Label Ecosystem 23

Poster: Longitudinal Measurement of the Adoption Dynamics in...

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30th acm SIGSAC conference on Computer and Communications security (acm CCS)

作者： Balash, David G. Ali, Mir Masood Kodwani, Monica Wu, Xiaoyuan Kanich, Chris Aviv, Adam J. Univ Richmond Richmond VA 23173 USA Univ Illinois Chicago IL 60680 USA George Washington Univ Washington DC 20052 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798400700507

this work reports on a large scale, longitudinal analysis of the adoption dynamics of privacy labels in the iOS App Store, measuring this first-of-its kind ecosystem as it reaches maturity over two and a half years after launching in December 2020. the motivation is to shed light on the factors affecting the shifts in privacy labels and provide insights into how and when an app's label changes. By collecting nearly weekly snapshots of over 1.6 million apps for over a year, we analyze the dynamics of privacy label adoption and the accuracy of reported labels. Our analysis of 74.5% of apps having labels after two years provides important context into this mature ecosystem where labels are becoming the standard. However, we find compelling evidence that labels may not fully capture behavior, as 28.9% of apps indicate no data collection and distributions differ between voluntary versus mandatory adoptions. Once set, labels rarely change but additions reflect more data collection. In addition to our measurement, we also plan to release a new (and growing) data set that can be used by future researchers.

关键词： privacy Labels iOS application App Marketplaces privacy

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Tool/dataset Paper: Realistic ABAC data Generation using Conditional Tabular GAN 23

Tool/Dataset Paper: Realistic ABAC Data Generation using Con...

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13th acm conference on data and application security and privacy (CODASPY)

作者： Rai, Ritwik Sural, Shamik Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798400700675

Attribute-based Access Control (ABAC) is increasingly being used in a wide variety of applications that include cloud services, IoT, smart homes, healthcare and several others. Conducting systematic and reproducible experiments with benchmark realistic datasets, however, still remains a challenge. To address this shortcoming, in this paper we introduce a method called ConGRASS (Conditional Tabular GAN for Realistic ABAC Simulation Studies) for generating large ABAC datasets. Starting with a given real world dataset of (potentially) limited size, we first train a conditional tabular generative adversarial network for learning its distribution. the trained model is used to generate realistic datasets of arbitrarily large sizes having distribution similar to the original dataset. ConGRASS has been implemented as a free to use web-based tool in which a user can choose the name of a listed real dataset along with the desired dataset size. A CSV file containing ABAC data is generated as output. Extensive evaluation shows the ability of the model to faithfully learn the statistical properties of the selected real data. When such a dataset is used in an actual problem, significant improvement in performance is achieved, proving the utility of ConGRASS.

关键词： Attribute-based Access Control (ABAC) Conditional GAN Tabular data generation ABAC simulation studies

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Guarding Identities on the Blockchain: A privacy-Centric Approach Leveraging ZK-SNARK and Shamir Secret Sharing 5th

Guarding Identities on the Blockchain: A Privacy-Centric App...

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

作者： Kommu, Siddartha Sudam, Rachana Rao, L. Sridhara Neeli, Shrenika JB Inst Engn & Technol Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

data protection assumes a pivotal role in the era of the Internet, given that numerous applications enable users to communicate and access online services. Anonymous identity verification system(AIV) is a system designed to effectively manage user identities while safeguarding their privacy. this system empowers users to verify their identities without revealing their actual personal information. Furthermore, AIV facilitates the disclosure of minimal identifiers while preserving selective confidentiality. However, the allure of anonymity also provides an incentive for malicious users to breach system rules and engage in illicit activities, as their true identities remain concealed. Traditional AIV systems rely on an identity provider (IP) for user identity verification. this approach records the actual identities of all users and allows for the tracking of malicious users. Consequently, user identity parameters are shielded out of all entities except IP address. Nonetheless, the privacy of individuals is jeopardized due to the authenticity of the IP address is not always guaranteed, and there is no assurance of the protection of user activity and private information. this article introduces a blockchain-based AIV scheme that empowers end-users for manage their identity parameters while keeping their true identities, including their IP addresses, confidential from all organizations. However, the system's consensus mechanism can aid in establishing the true identities of perpetrators in cases of rule violations. security requirements for AIV are informally analyzed and confirmed using a game-based verification system. the core concept of this research revolves around the integration of zk-SNARK,zero knowledge sunnict non-iterative argument of knowledge a form of ZKP, Shamir's threshold secret distribution Sharing, and various different secret coding approaches.

关键词： block chain anonymous privacy ZKP secret sharing

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A Lightweight security Technique for Processing Large data Over a Distributed Cloud Environment 5

A Lightweight Security Technique for Processing Large Data O...

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5th International conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Saxena, Vivek Bhattacharjee, Shiladitya Saxena, Deepika Poornima Institute of Engineering and Technology Jaipur India School of Computer Science UPES Dehradun India Poornima University Jaipur India

ISBN: (纸本)9798400709418

It is never easy to guarantee data security when processing on a distributed cloud platform. data integrity and privacy are the two most crucial issues in any cloud-based data processing system. Various errors and security breaches during the processing and transfer of data might compromise data integrity and privacy. the criticality increases with the magnitude of the supplied data. Numerous studies have been conducted to find an integrated solution to these problems. Nevertheless, there is currently no workable approach to address any of these problems in a combinatorial manner in the literature. Consequently, to address these problems, this research effort presents a novel, lightweight, and integrated homomorphic encryption method. the proposed method raises degree of unpredictability throughout the encryption to sufficiently protect data privacy. Additionally, it has a special Method for error identification and correction to improve data integrity. the research outcomes justify its superiority over other similar existing techniques in the literature. © 2023 acm.

关键词： Distributed cloud

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application of Homomorphic Encryption for Encrypting and Decrypting Patient data in thailand's Healthcare System 5

Application of Homomorphic Encryption for Encrypting and Dec...

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5th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2024

作者： Lohlah, Yadzaree Boonyopakorn, Pongsarun King Mongkut's University of Technology North Bangkok Department of Digital Network and Information Security Management Bangkok Thailand

ISBN: (纸本)9798331505998

Homomorphic Encryption (HE) is an advanced cryptographic technique in computer science that enables the processing of encrypted data without the need for decryption. It is a significant component of privacy-Enhancing Technologies (PETs) aimed at ensuring the privacy and security of data. this study aims to explore and evaluate the capabilities of HE when applied to patient data within a healthcare system, providing valuable insights for the effective implementation of HE. the research involves creating a simulated environment that leverages the TenSEAL Library to utilize HE, and a dataset comprising patient information, which includes numerical, textual data, and thai characters. the study employs Fully Homomorphic Encryption (FHE) with the CKKS scheme. the results indicate that the performance of data encryption and decryption using FHE CKKS is highly accurate. this depends on the appropriate setting of the context modulus parameter. CKKS encryption demonstrates a strong ability to handle both numerical data and text encryption effectively, maintaining high accuracy of the processed data. However, handling textual data requires further refinement in parameter settings and managing complex encryption processes. In conclusion, HE shows significant potential for protecting patient data in healthcare systems. this study lays a solid foundation for further research and development to enhance the suitability and efficiency of HE technology in the future. © 2024 IEEE.

关键词： Differential privacy

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privacy computing technology in the context of energy Internet 5

Privacy computing technology in the context of energy Intern...

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5th International conference on Communications, Information System and Computer Engineering, CISCE 2023

作者： Gong, Yue Chen, Cuiyun Liu, Zhiyu The First Research Institute of the Ministry of Public Security of P.R.C Beijing China

ISBN: (纸本)9798350326796

the power system and other energy systems are constantly cross-integrated, and a new development direction of multi-subject collaboration is gradually formed. Starting from the typical mode of energy Internet collaborative optimization, this paper puts forward a preliminary technical scheme for energy Internet collaborative optimization considering privacy protection. Aiming at the scenario without a third party, this paper proposes a secure multi-party interactive computing scheme based on the principle of secret sharing, and analyzes the specific characteristics of the scheme when applied to a small number of subject scenarios based on the fully distributed application architecture. Finally, from the aspects of data privacy and computing security, the proposed scheme protects the rights and interests of data owners, reduces the risk of data leakage in energy Internet collaborative optimization computing, and realizes reliable privacy protection. © 2023 IEEE.

关键词： data privacy

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Customizable Component-Based application Assembly with Storage Algorithm 5

Customizable Component-Based Application Assembly with Stora...

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

作者： Suganya, A. Nagarasu, P. Siva, S. Vignesh, M. Karpagam college of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331530013

AegisLibre is a novel decentralized storage algorithm designed to prioritize high security and efficiency for sensitive data like personal information, text, photos, and other private content. Drawing inspiration from blockchain technology, it combines dynamic encryption, smart contracts, zero-knowledge proofs (ZKP), and Proof of Storage (PoS) to offer a highly secure and verifiable storage system. this paper explores the foundations of AegisLibre, compares it with existing algorithms such as IPFS, discusses its key features, and presents an analysis of its security capabilities and performance. © 2024 IEEE.

关键词： Storage efficiency

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