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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61-70 订阅

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Demo : data Minimization and Informed Consent in Administrative Forms 23

Demo : Data Minimization and Informed Consent in Administrat...

引用

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

作者： Anciaux, Nicolas Frittella, Sabine Joffroy, Baptiste Nguyen, Benjamin INRIA Saclay France Univ Paris Saclay Versailles Saclay France INSA Ctr Val Loire LIFO Bourges France Univ Orleans Orleans France

ISBN: (纸本)9798400700507

this article proposes a demonstration implementing the data minimization privacy principle, focusing on reducing data collected by government administrations through forms. data minimization is defined in many privacy regulations worldwide, but has not seen extensive real-world application. We propose a model based on logic and game theory and show that it is possible to create a practical and efficient solution for a real French welfare benefit case.

关键词： privacy data minimization GDPR Informed consent

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Research on the application of Blockchain Consensus Mechanisms in Federated Learning for privacy Protection 5

Research on the Application of Blockchain Consensus Mechanis...

引用

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

作者： Hu, Xuehui Chu, Xuesen Hong, Huajun Guo, Wei Wu, Tianqi Li, Jinku Shanghai Tongtai Information Technology Co. Ltd. Shanghai200235 China China Ship Scientific Research Center Wuxi214082 China School of Cyber Engineering Xidian University Xi'an710071 China

ISBN: (纸本)9798331539818

Federated learning, as a distributed machine learning method is facing significant challenges in data privacy protection at present. this study aims to enhance the privacy protection capability of federated learning by integrating blockchain consensus mechanisms, as well as proposing a novel solution by designing a decentralized federated learning framework using consensus mechanisms such as Proof of Stake (PoS), and implementing smart contracts to automate key processes. the research results demonstrate that this approach effectively enhances the security and transparency of data processing while ensuring the efficiency and scalability of the system. Furthermore, the adoption of homomorphic encryption technology further ensures the security and integrity of data during transmission. © 2024 IEEE.

关键词： Federated learning

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privacy-Preserving Fully Online Matching with Deadlines 23

Privacy-Preserving Fully Online Matching with Deadlines

引用

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

作者： Klinger, Andreas Meyer, Ulrike Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9798400700675

In classical secure multi-party computation (SMPC) it is assumed that a fixed and a priori known set of parties wants to securely evaluate a function of their private inputs. this assumption implies that online problems, in which the set of parties that arrive and leave over time are not a priori known, are not covered by the classical setting. therefore, the notion of online SMPC has been introduced, and a general feasibility result has been proven that shows that any online algorithm can be implemented as a distributed protocol that is secure in this setting [22, 23]. However, so far, no online SMPC protocol that implements a concrete online algorithm has been proposed and evaluated such that the practicality of the constructive proof is an open question. We close this gap and propose the first privacy-preserving online SMPC protocol for the prominent problem of fully online matching with deadlines. In this problem an (a priori unknown) set of parties with their inputs arrive over time and can then be matched with other parties until they leave when their individual deadline is reached. We prove that our protocol is statistically secure in the presence of a semi-honest adversary that controls strictly less than half of the parties present at each point in time. We extensively evaluate the performance of our protocol in three different network settings, various input sizes and different matching conditions, as well as various numbers of parties.

关键词： SMPC online matching privacy

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Blockchain transaction data privacy protection scheme 5

Blockchain transaction data privacy protection scheme

引用

5th International conference on Computer Communication and Network security, CCNS 2024

作者： Li, Yifeng Beijing University of Technology Beijing China

ISBN: (纸本)9781510681842

Blockchain is a shared digital ledger that, once transactions are recorded and verified, cannot be altered. Since most blockchain nodes maintain a copy of the ledger, blockchain brings the benefit of decentralization but also introduces privacy concerns. this paper explores the privacy issues associated with blockchain transaction data, primarily using zero-knowledge proof technology to design a privacy protection scheme. this scheme utilizes Pedersen commitments and Bulletproofs zero-knowledge proof technology to prevent the disclosure of transaction information while ensuring the correctness and legality of transaction data. security analysis of the scheme demonstrates that it effectively addresses problems such as double-spending, payment authorization, and dishonest miners, thereby enhancing blockchain performance while protecting the privacy of blockchain transaction data. © 2024 SPIE.

关键词： Differential privacy

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IdDecoder: A Face Embedding Inversion Tool and its privacy and security Implications on Facial Recognition Systems 23

IdDecoder: A Face Embedding Inversion Tool and its Privacy a...

引用

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

作者： Minh-Ha Le Carlsson, Niklas Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798400700675

Most state-of-the-art facial recognition systems (FRS:s) use face embeddings. In this paper, we present the IdDecoder framework, capable of effectively synthesizing realistic-neutralized face images from face embeddings, and two effective attacks on state-of-the-art facial recognition models using embeddings. the first attack is a black-box version of a model inversion attack that allows the attacker to reconstruct a realistic face image that is both visually and numerically (as determined by the FRS:s) recognized as the same identity as the original face used to create a given face embedding. this attack raises significant privacy concerns regarding the membership of the gallery dataset of these systems and highlights the importance of both the people designing and deploying FRS:s paying greater attention to the protection of the face embeddings than currently done. the second attack is a novel attack that performs the model inversion, so to instead create the face of an alternative identity that is visually different from the original identity but has close identity distance (ensuring that it is recognized as being of the same identity). this attack increases the attacked system's false acceptance rate and raises significant security concerns. Finally, we use IdDecoder to visualize, evaluate, and provide insights into differences between three state-of-the-art facial embedding models.

关键词： Face embedding inversion Black-box attack Facial recognition

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Swipe2Pair: Secure and Fast In-Band Wireless Device Pairing 24

Swipe2Pair: Secure and Fast In-Band Wireless Device Pairing

引用

17th acm conference on security and privacy in Wireless and Mobile Networks (WiSec)

作者： He, Yaqi Zeng, Kai Jiao, Long Mark, Brian L. Khasawneh, Khaled N. George Mason Univ Wireless Cyber Ctr Fairfax VA 22030 USA Univ Massachusetts Dartmouth Dept Comp Informat Sci Dartmouth NS Canada

ISBN: (纸本)9798400705823

Wireless device pairing is a critical security mechanism to bootstrap the secure communication between two devices without a pre-shared secret. It has been widely used in many Internet of things (IoT) applications, such as smart-home and smart-health. Most existing device pairing mechanisms are based on out-of-band channels, e.g., extra sensors or hardware, to validate the proximity of pairing devices. However, out-of-band channels are not universal across all wireless devices, so such a scheme is limited to certain application scenarios or conditions. On the other hand, in-band channel-based device pairing seeks universal applicability by only relying on wireless interfaces. Existing in-band channel-based pairing schemes either require multiple antennas separated by a good distance on one pairing device, which is not feasible in certain scenarios, or require users to repeat multiple sweeps, which is not optimal in terms of usability. therefore, an in-band wireless device pairing scheme providing high security while maintaining high usability (simple pairing process and minimal user intervention) is highly desired. In this work, we propose an easy-to-use mutual authentication device pairing scheme, named Swipe2Pair, based on the proximity of pairing devices and randomization of wireless transmission power. We conduct extensive security analysis and collect considerable experimental data under various settings across different environments. Experimental results show that Swipe2Pair achieves high security and usability. It only takes less than one second to complete the pairing process with a simple swipe of one device in front of the other.

关键词： security Protocol Authentication In-band Device Pairing Wireless Communication System

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Federated Learning for privacy Preserving Intelligent Healthcare application to Breast Cancer Detection 25

Federated Learning for Privacy Preserving Intelligent Health...

引用

26th International conference on Distributed Computing and Networking, ICDCN 2025

作者： Amritanjali Gupta, Ragav Birla Institute of Technology Mesra Ranchi Ranchi India Sri Mata Vaishno Devi University Jammu Katra India

ISBN: (纸本)9798400710629

Health data used in building intelligent medical care systems contain sensitive personal information of the patients. Distributed storage, processing and sharing of data poses threats to the data privacy and security. Federated learning (FL) is an emerging approach that allows multiple entities to collaboratively train a model without sharing their data, thus preserving data privacy. this paper explores the implementation of federated learning techniques for deep learning-based breast cancer detection using the publicly available BRCA dataset. the efficacy of both centralized and federated approaches has been examined through various performance metrics. this work is an effort to reduce the gap between privacy-preserving techniques and practical healthcare applications, offering insights into the trade-offs between data privacy and model performance. © 2025 Copyright held by the owner/author(s).

关键词： Federated learning

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application Status, Problems and Future Prospects of Generative AI in Education 5

Application Status, Problems and Future Prospects of Generat...

引用

5th International conference on Computer Science and Technologies in Education, CSTE 2023

作者： Yu, Hao Liu, Zhaoyang Guo, Yunyun Shaanxi Normal University Department of Education Xi'an China School of Foreign Languages Northwest University Xi'an China

ISBN: (纸本)9798350325720

the emergence of Chat GPT signifies another revolutionary wave of information technology brought about by generative AI. this article introduces the development and technical support of generative AI, revealing that there are four main issues regarding its application in the education field: opacity and inexplicability, data privacy and security, individualization and fairness, and effectiveness and reliability. the article then looks forward to the future trends of generative AI in the education field from four aspects: personalized education, intelligent teaching, joint education, and virtual teaching, aiming to provide important reference value for research and practice in this field. © 2023 IEEE.

关键词： data privacy

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Anonymous System for Fully Distributed and Robust Secure Multi-Party Computation 23

Anonymous System for Fully Distributed and Robust Secure Mul...

引用

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

作者： Klinger, Andreas Battermann, Felix Meyer, Ulrike Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9798400700675

In secure multi-party computation (SMPC), it is considered that multiple parties that are known to each other evaluate a function over their private inputs in a secure fashion. the participating parties do not learn anything about each other's private inputs beyond what can be deduced from their own input and output. the assumption that the parties know each other, however, does not seem suitable for all potential applications of SMPC. In some applications participants may not only want to hide their private inputs and outputs, but may also want to hide the fact that they are participating in a given function evaluation in the first place. We therefore propose an anonymous system for SMPC that allows parties to anonymously evaluate a function of their private inputs in a fully distributed and secure fashion. the proposed system allows authorized parties to execute an SMPC protocol robust with penalty against a dishonest majority in the presence of a malicious adversary. During the protocol execution, the system guarantees that all participating parties stay anonymous w.r.t. each other as well as any third parties. In addition, it guarantees that in each function evaluation all participating parties are unique, i.e., no party can participate as more than one entity.

关键词： SMPC anonymity privacy

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Improved Layered Architecture: Integration of IoT with Blockchain for Increased security and Scalability 5

Improved Layered Architecture: Integration of IoT with Block...

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5th International conference on Artificial Intelligence and data Sciences, AiDAS 2024

作者： Jahangir, Muhammad Lee, Ming Jie Kwan, Ban-Hoe Kamal, Tariq Chai, Tong-Yuen Loh, Wing Son Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia Dhanani School of Science & Engineering Electrical and Computer Engineering Karachi Pakistan

ISBN: (纸本)9798331528553

Most of the existing Internet of things (IoT) applications are built upon the conventional three-layered architecture that possesses several issues in terms of security, privacy and scalability. In this paper, we propose a five-layer architecture to mitigate the issues faced by the three-layered IoT applications. In particular, two layers: (i) data-link layer and (ii) IoT-Blockchaln layer were introduced. the data-link layer authenticates the legitimate device during the data transmission;while the IoT-Blockchain utilizes the distributed storage to prevent the data from being tampered. To testify the performance of the proposed method in terms of publishing time, experiments were conducted based on two setups: (i) single node setup and (ii) dual node setup. the results suggested the proposed method is highly scalable where the single node setup required an average of 9.4 seconds and dual node setup required 8.66 seconds. Future research will explore various network configurations and the integration of the Internet of things application (IOTA), a feeless and lightweight blockchain, to further optimize scalability and efficiency. © 2024 IEEE.

关键词： Differential privacy

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