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A Decentralized Privacy Preserving Reputation Protocol for the Malicious Adversarial Model

O. Hasan, L. Brunie, E. Bertino, Ning Shang
2013 IEEE Transactions on Information Forensics and Security  
We present a privacy preserving reputation protocol for the malicious adversarial model.  ...  A privacy preserving reputation protocol protects users by hiding their individual feedback and revealing only the reputation score.  ...  In a previous paper [2] , we presented the non-cryptographic k-shares decentralized privacy preserving reputation protocol for the semi-honest adversarial model.  ... 
doi:10.1109/tifs.2013.2258914 fatcat:ubgicg5xyzfgroa5kd2oh5yqvu

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Shahid Habib, Ghaffer Iqbal Kiani, Muhammad Fasih Uddin Butt, Syed Muzahir Abbas, Abdulah Jeza Aljohani, Soon Xin Ng
2022 Computers Materials & Continua  
In this paper, we propose a hybrid privacy-preserving scheme for federated learning, called Fed-DFE. Specifically, we present a decentralized multi-client function encryption algorithm.  ...  With a lightweight encryption overhead, function encryption is a viable secure aggregation technique in federation learning, which is often used in combination with differential privacy.  ...  There is still a gap between game theory and secure aggregation in federated learning. In this paper, we propose a hybrid privacy-preserving framework for federated learning called Fed-DFE.  ... 
doi:10.32604/cmc.2022.022290 fatcat:bfzgrjswdbgn7hbkzoqmptyxja

DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting [article]

Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Swanand Kadhe, Heiko Ludwig
2022 arXiv   pre-print
This paper presents DeTrust-FL, an efficient privacy-preserving federated learning framework for addressing the lack of transparency that enables isolation attacks, such as disaggregation attacks, during  ...  Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data.  ...  To address the previously described limitations, we propose DeTrust-FL, an efficient, scalable, and secure aggregation based privacy-preserving federated learning decentralized trust approach, that solves  ... 
arXiv:2207.07779v1 fatcat:eaj77iajpzckbfbpwq3zgmuapy

Regulating Ownership Verification for Deep Neural Networks: Scenarios, Protocols, and Prospects [article]

Fang-Qi Li, Shi-Lin Wang, Alan Wee-Chung Liew
2021 arXiv   pre-print
Numerous watermarking schemes have been proposed to identify the owner of a deep neural network and verify the ownership.  ...  To bridge the gap between those proposals and real-world demands, we study the deep learning model intellectual property protection in three scenarios: the ownership proof, the federated learning, and  ...  Analogously, we can define verifier-privacy-preserving. The key-privacy-preserving properties suggests that the verify module of the watermarking scheme should depend on key.  ... 
arXiv:2108.09065v1 fatcat:sowx6ks54rc2nau47eqb264kaq

Privacy Preserving Reputation Management in Social Networks [chapter]

Omar Hasan, Lionel Brunie
2013 Lecture Notes in Social Networks  
In particular we will look at privacy preserving reputation management in decentralized social networks, where there is no central authority or trusted third parties, thus making the task of preserving  ...  Privacy preserving reputation systems hide the individual ratings of users about others and only reveal the aggregated community reputation score thus allowing users to rate without the fear of retaliation  ...  Decentralized Privacy Preserving Reputation Systems In the following sections, we discuss reputation systems that can be deployed in decentralized social networks for privacy preserving reputation management  ... 
doi:10.1007/978-3-7091-0894-9_8 dblp:series/lnsn/HasanB13 fatcat:dyeswkwtmvfurgsefr7y64lgga

M2M-REP: Reputation system for machines in the internet of things

Muhammad Ajmal Azad, Samiran Bag, Feng Hao, Khaled Salah
2018 Computers & security  
The design of a reliable reputation system for the distributed M2M communication network should preserve user privacy and have low computation and communication overheads.  ...  The M2M-REP system ensures correctness, security and privacy properties under the malicious adversarial model, and allows public verifiability without relying on a centralized trusted system.  ...  We thank the anonymous reviewers for their invaluable comments and suggestions towards improving this paper.  ... 
doi:10.1016/j.cose.2018.07.014 fatcat:hqka4u23aff2rlhyh53pyfeu3a

PDS2: A user-centered decentralized marketplace for privacy preserving data processing

Lodovico Giaretta, Ioannis Savvidis, Thomas Marchioro, Sarunas Girdzijauskas, George Pallis, Marios D. Dikaiakos, Evangelos Markatos
2021 Zenodo  
In order to achieve this, our marketplace architecture employs blockchain technology, privacy-preserving computation and decentralized machine learning.  ...  The goal of PDS2is to ensure that users maintain full control on their data and do not compromise their privacy, while being rewarded for the value that their data generates.  ...  Recently, many machine learning frameworks for both privacy preserving training and inference have been developed.  ... 
doi:10.5281/zenodo.4650251 fatcat:f7llanx5ibcrjjdvilwzlr2jfa

PDS2: A user-centered decentralized marketplace for privacy preserving data processing

Lodovico Giaretta, Ioannis Savvidis, Thomas Marchioro, Sarunas Girdzijauskas, George Pallis, Marios D. Dikaiakos, Evangelos Markatos
2021 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW)  
In order to achieve this, our marketplace architecture employs blockchain technology, privacypreserving computation and decentralized machine learning.  ...  The goal of PDS 2 is to ensure that users maintain full control on their data and do not compromise their privacy, while being rewarded for the value that their data generates.  ...  Recently, many machine learning frameworks for both privacy preserving training and inference have been developed.  ... 
doi:10.1109/icdew53142.2021.00024 fatcat:oxlanrzs25bplhz5kt7vzqrjei

Reliable Federated Learning for Mobile Networks [article]

Jiawen Kang, Zehui Xiong, Dusit Niyato, Yuze Zou, Yang Zhang, Mohsen Guizani
2019 arXiv   pre-print
providing privacy preservation for mobile users.  ...  Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering.  ...  Mobile applications with federated learning perform model training by using these data without the need of data aggregation for privacy preservation.  ... 
arXiv:1910.06837v1 fatcat:txiaqizl4zgsngiqusz2h6sv2a

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing [chapter]

Yao Du, Shuxiao Miao, Zitian Tong, Victoria Lemieux, Zehua Wang
2021 Blockchain Potential in AI [Working Title]  
As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization.  ...  However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security.  ...  Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is a type of non-interactive ZKP and has been used by cryptocurrency Zcash as its core privacy-preserving mechanism.  ... 
doi:10.5772/intechopen.96618 fatcat:tjon4elghrcq5e5lfsfmyx2srm

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets [article]

David Froelicher, Juan R. Troncoso-Pastoriza, Joao Sa Sousa and Jean-Pierre Hubaux
2020 arXiv   pre-print
In fact, sharing personal information requires individuals' unconditional consent or is often simply forbidden for privacy and security reasons.  ...  In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets.  ...  ACKNOWLEDGMENT The authors would like to thank Henry Corrigan-Gibbs and all members of the Laboratory for Data Security at EPFL for their helpful feedback and their support.  ... 
arXiv:1902.03785v3 fatcat:jhs2wwxf3jgpxnc7hio7c5xcf4

Security and Privacy in Vehicular Social Networks [article]

Hongyu Jin and Mohammad Khodaei and Panos Papadimitratos
2020 arXiv   pre-print
We surveyed and presented the state-of-the-art VC systems, security and privacy architectures and technologies, emphasizing on security and privacy challenges and their solutions for P2P interactions in  ...  This is especially so, considering (i) the privacy risk from the exposure of the users to the service providers, and (ii) the security risk from the interaction with malicious or selfish and thus misbehaving  ...  OPEN CHALLENGES Based on and beyond the existing solutions for security and privacy preserving VSNs, there still exist a number of significant security and privacy challenges towards deploying such VSNs  ... 
arXiv:2001.08014v1 fatcat:7lydpc4ixjgtxl7ynfoge6w4n4

A privacy-aware decentralized and personalized reputation system

Samiran Bag, Muhammad Ajmal Azad, Feng Hao
2018 Computers & security  
We have analyzed the security and privacy properties of the scheme for the malicious adversarial model.  ...  To this extent, the participants share cryptograms of their trust-scores for the business entity to the decentralized public bulletin board or tally center.  ...  We thank the anonymous reviewers for their valuable comments and suggestions towards improving the quality of this paper.  ... 
doi:10.1016/j.cose.2018.05.005 fatcat:7zveszgyfzgqnhr75xalnd6xvi

Federated Learning Meets Natural Language Processing: A Survey [article]

Ming Liu, Stella Ho, Mengqi Wang, Longxiang Gao, Yuan Jin, He Zhang
2021 arXiv   pre-print
Federated Learning aims to learn machine learning models from multiple decentralized edge devices (e.g. mobiles) or servers without sacrificing local data privacy.  ...  Our survey discusses major challenges in federated natural language processing, including the algorithm challenges, system challenges as well as the privacy issues.  ...  The proposed decentralized framework takes advantage of the quantum learning progress to secure models and to avoid privacy leakage attacks.  ... 
arXiv:2107.12603v1 fatcat:ebi4i6jnxbhihe7zuqx4uposbm

Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI [article]

Jiawen Kang, Dongdong Ye, Jiangtian Nie, Jiang Xiao, Xianjun Deng, Siming Wang, Zehui Xiong, Rong Yu, Dusit Niyato
2022 arXiv   pre-print
In this paper, we design a user-defined privacy-preserving framework with decentralized federated learning for the industrial metaverses.  ...  To further improve privacy protection of industrial metaverse, a cross-chain empowered federated learning framework is further utilized to perform decentralized, secure, and privacy-preserving data training  ...  To further improve privacy protection of industrial metaverse, a cross-chain empowered federated learning framework is further utilized to perform decentralized, secure, and privacy-preserving data training  ... 
arXiv:2206.07384v2 fatcat:kpkszbndn5evlo4fz2yopyooei
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