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A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
[article]
2022
arXiv
pre-print
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In this survey, we provide a comprehensive review of recent leading approaches in the TwGL field from three dimensions, namely, reliability, explainability, and privacy protection. ...
Trustworthy graph learning (TwGL) aims to solve the above problems from a technical viewpoint. ...
This survey provides a systematic and comprehensive overview of trustworthy graph learning (TwGL) from three fundamental aspects, i.e., reliability, explainability and privacy. ...
arXiv:2205.10014v2
fatcat:aobv34rwg5ehpka4fsuar2gm7i
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context
[article]
2022
arXiv
pre-print
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We conclude with an identification of open research problems, with a particular focus on the connection between trustworthy machine learning technologies and their implications for individuals and society ...
As a consequence, we survey in this paper the main technologies with respect to all four of the FEAS properties, for data-centric as well as model-centric stages of the machine learning system life cycle ...
We do this through a survey that considers technologies for trustworthy machine learning in relation to emerging policy frameworks. ...
arXiv:2007.08911v3
fatcat:gmswdvel6bdbvg5rvyzb2uygbu
A Survey on Trust Evaluation Based on Machine Learning
2020
ACM Computing Surveys
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In this article, we perform a thorough survey on trust evaluation based on machine learning. First, we cover essential prerequisites of trust evaluation and machine learning. ...
It faces a number of severe issues such as lack of essential evaluation data, demand of big data process, request of simple trust relationship expression, and expectation of automation. ...
Fourth, the security of trust evaluation is worth special efforts. In our survey, the issue of data privacy protection has not been touched. ...
doi:10.1145/3408292
fatcat:fem3px673bcfdltackc7bstxji
Machine Learning for Security in Vehicular Networks: A Comprehensive Survey
[article]
2021
arXiv
pre-print
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Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains. ...
In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks. ...
Privacy Protection Privacy is a means of protecting the sensitive information of vehicles from attackers. ...
arXiv:2105.15035v2
fatcat:5z6aqlvosjgf3o3amts3k6toxu
Federated Learning for Smart Healthcare: A Survey
[article]
2021
arXiv
pre-print
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Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. ...
Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. ...
, while a degree of privacy protection is achieved. ...
arXiv:2111.08834v1
fatcat:jmex4e25rbgy3bk67iolrj4uee
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
[article]
2022
arXiv
pre-print
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Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering ...
In this survey, we introduce basic concepts and comprehensively summarise existing efforts for trustworthy GNNs from six aspects, including robustness, explainability, privacy, fairness, accountability ...
BETWEEN OUR SURVEY AND REPRESENTATIVE SURVEYS Scope Perspective Surveys Generic GNNs Trustworthiness AI GNNs Robust-ness Explain-ability Privacy Fairness Account-ability Environment-al Well-being Relations ...
arXiv:2205.07424v1
fatcat:f3iul7soqvgzbgaeqw7nhypbju
AI System Engineering—Key Challenges and Lessons Learned
2020
Machine Learning and Knowledge Extraction
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The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. ...
and ethical aspects. the analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software ...
The complex of problems that derives from this aspect of artificial intelligence for explainability, transparency, trustworthiness, and so forth, is generally described with the term Explainable Artificial ...
doi:10.3390/make3010004
fatcat:35qfecqrn5auxc3epcjodxuez4
Trustworthy AI: A Computational Perspective
[article]
2021
arXiv
pre-print
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In this survey, we present a comprehensive survey of trustworthy AI from a computational perspective, to help readers understand the latest technologies for achieving trustworthy AI. ...
In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability ...
Most importantly, a versatile and reliable system design for achieving privacy protection and different techniques should be integrated to enhance the trustworthiness of AI systems. ...
arXiv:2107.06641v3
fatcat:ymqaxvzsoncqrcosj5mxcvgsuy
A Bidder Behavior Learning Intelligent System for Trust Measurement
2014
International Journal of Computer Applications
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The online auctions face a serious problem of trust among the participants where users have no information about the others, and have no relations among them except the commercial transactions; this allows ...
We proposed a framework depend on "Bidder Behavior" consist of three algorithms the first " Bidder Increase Price Behavior"(BIPB), the second "Search In Stored Data Base"(SSDB) and the third "Person Recursive ...
transactions as a social network graph which edges length and thickness as a function of strength and goodness. ...
doi:10.5120/15521-4225
fatcat:q3do4zfu3nfj3cj45ur3njcgji
Privacy-preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation
[article]
2019
arXiv
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Further, the ADMM algorithm is perturbed through a combined noise-adding method, which simultaneously preserves privacy for users' less sensitive information and strengthens the privacy protection of the ...
With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. ...
To protect privacy of feature vectors against less trustworthy servers, we further use a combined noise-adding method to perturb the ADMM algorithm, which also strengthens the privacy guarantee of users ...
arXiv:1908.01059v2
fatcat:6n7ex5esh5cg3hthdpic6zoqje
Blockchain-based Federated Learning: A Comprehensive Survey
[article]
2021
arXiv
pre-print
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However, issues of privacy and scalability will constrain the development of machine learning. ...
In this paper, we conduct a comprehensive survey of the literature on blockchained FL (BCFL). ...
[20] surveys the research related to the privacy issues of FL, illustrating several attacks which will lead to the leakage of data privacy, e.g., membership inference attack and GAN-based(a deep learning ...
arXiv:2110.02182v1
fatcat:sm2mtftvq5fodgkfdhcan55n3q
Deep Learning Application in Security and Privacy -- Theory and Practice: A Position Paper
[article]
2018
arXiv
pre-print
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We also put forward potential challenges a DL based security and privacy protection technique has to overcome. ...
Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. ...
Security and Privacy by Deep Learning In this section, we survey the types of security and privacy services and applications in which DL is deployed successfully -as represented by academic literature. ...
arXiv:1812.00190v1
fatcat:jkv2z6mjmvcbvo2l3ptassfpp4
A Survey on Trustworthy Recommender Systems
[article]
2022
arXiv
pre-print
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In this survey, we will introduce techniques related to trustworthy and responsible recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation ...
Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research ...
Explainable AI and Privacy. Some existing privacy protection techniques are built based on deep learning models that are difficult to interpret. ...
arXiv:2207.12515v1
fatcat:lsnuwdtl5rboznmhhux2n5y5om
Towards Security Threats of Deep Learning Systems: A Survey
[article]
2020
arXiv
pre-print
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In order to unveil the security weaknesses and aid in the development of a robust deep learning system, we undertake an investigation on attacks towards deep learning, and analyze these attacks to conclude ...
However, deep learning systems are suffering several inherent weaknesses, which can threaten the security of learning models. Deep learning's wide use further magnifies the impact and consequences. ...
The privacy protection terms used in related articles are explained in detail in Section 8.6. ...
arXiv:1911.12562v2
fatcat:m3lyece44jgdbp6rlcpj6dz2gm
Deep Learning for Android Malware Defenses: a Systematic Literature Review
[article]
2022
arXiv
pre-print
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In recent years, a dominant research field called deep learning (DL), which provides a powerful feature abstraction ability, has demonstrated a compelling and promising performance in a variety of areas ...
In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment. ...
CCS Concepts: • Security and privacy → Malware and its mitigation; Software security engineering; • General and reference → Surveys and overviews. ...
arXiv:2103.05292v2
fatcat:qruddq4gknfq7jx5wyrk5qu2eu
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