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2018 Index IEEE Transactions on Knowledge and Data Engineering Vol. 30

2019 IEEE Transactions on Knowledge and Data Engineering  
Wang, X., Differentially Private Distributed Online Learning. Li, C., þ, TKDE Aug. 2018 1440-1453 FEDERAL: A Framework for Distance-Aware Privacy-Preserving Record Linkage.  ...  ., þ, TKDE Dec. 2018 2285-2297 Authorization Authenticating Aggregate Queries over Set-Valued Data with Confidentiality. Differentially Private Distributed Online Learning.  ... 
doi:10.1109/tkde.2018.2882359 fatcat:asiids266jagrkx5eac6higrlq

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges [article]

Bin Jiang, Jianqiang Li, Guanghui Yue, Houbing Song
2021 arXiv   pre-print
Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging.  ...  In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT.  ...  And Zhou et al. focused on online social multimedia big data for privacy-preserving method [169] . B.  ... 
arXiv:2101.10569v2 fatcat:xwebjbcvcjhbbaehajhdfjocia

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Zhaoyang Du, Celimuge Wu, Tsutomu Yoshinaga, Kok-Lim Alvin Yau, Yusheng Ji, Jie Li
2020 IEEE Computer Graphics and Applications  
Federated learning (FL) is a distributed machine learning approach that can achieve the purpose of collaborative learning from a large amount of data that belong to different parties without sharing the  ...  Future vehicular Internet of Things (IoT) systems, such as cooperative autonomous driving and intelligent transport systems (ITS), feature a large number of devices and privacy-sensitive data where the  ...  [35] propose a FL framework for social recommender systems.  ... 
doi:10.1109/ojcs.2020.2992630 pmid:32386144 fatcat:uic45awlkneihkybfu7wnc26me

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

Tan Li, Congduan Li, Jingjing Luo, Linqi Song
2020 Intelligent and Converged Networks  
Internet of Vehicles (IoV) is a distributed network of connected cars, roadside infrastructure, wireless communication networks, and central cloud platforms.  ...  In this paper, we review some of the key techniques in recommendations and discuss what are the opportunities and challenges to deploy these wireless recommendations in the IoV.  ...  [110] , we studied an anomaly detection problem in the federated learning framework where many distributed nodes are trying to protect the privacy of their local data against others.  ... 
doi:10.23919/icn.2020.0005 fatcat:r7xdwyanrvfcxivff4k5pddswm

Ethical and legal considerations of artificial intelligence and algorithmic decision-making in personalized pricing

Joshua A. Gerlick, Stephan M. Liozu
2020 Journal of Revenue and Pricing Management  
The legal frameworks of antitrust, data privacy, and antidiscrimination are tethered to the ethical frameworks of deception, fairness, and social justice to form a foundation of relevant organizational  ...  A qualitative study is proposed to test the validity of the conceptual model.  ...  There also remains a dearth of literature on the potential discriminatory impacts of personalized pricing-particularly in combination with big data analytics, AI, and machine learning algorithms.  ... 
doi:10.1057/s41272-019-00225-2 fatcat:wilxi7dsgzcgrbpprdakl35jbe

Federated Data: Toward New Generation of Credible and Trustable Artificial Intelligence

Fei-Yue Wang, Weishan Zhang, Yonglin Tian, Rui Qin, Xiao Wang, Bin Hu
2021 IEEE Transactions on Computational Social Systems  
Federated data also provide an effective solution for data security and privacy issues, in either centralized or distributed manner.  ...  For privacy-preserving, federated data are divided into private data and non-private data, and through the federated control of these data, data federalization can be realized [item 3) in the Appendix]  ... 
doi:10.1109/tcss.2021.3077033 fatcat:ojozfayjjjgkzn3a7xikrgfhqy

Privacy Intelligence: A Survey on Image Privacy in Online Social Networks [article]

Chi Liu, Tianqing Zhu, Jun Zhang, Wanlei Zhou
2021 arXiv   pre-print
Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion.  ...  The framework consists of three stages with different principles of privacy by design.  ...  The distributed learning frameworks such as collaborative learning [267] - [269] and federated learning [270] may be candidates, as they develop independent models locally without data sharing, then  ... 
arXiv:2008.12199v2 fatcat:vxv6rsnyavesjiw2bbnc4jzsiy

When Machine Learning Meets Privacy

Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, Zihuai Lin
2021 ACM Computing Surveys  
Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era.  ...  It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both  ...  -Federated learning: A popular framework for collaborative learning is Federated learning [69] introduced by Google.  ... 
doi:10.1145/3436755 fatcat:cbkbmxj7krc3xoedv6tan4fle4

When Machine Learning Meets Privacy: A Survey and Outlook [article]

Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, Zihuai Lin
2020 arXiv   pre-print
Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era.  ...  It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both  ...  -Federated learning: A popular framework for collaborative learning is Federated learning [69] introduced by Google.  ... 
arXiv:2011.11819v1 fatcat:xuyustzlbngo3ivqkc4paaer5q

Contextual Integrity Up and Down the Data Food Chain

Helen Nissenbaum
2019 Theoretical Inquiries in Law  
Mounting challenges from a burgeoning array of networked, sensor-enabled devices (IoT) and data-ravenous machine learning systems, similar in form though magnified in scope, call for renewed attention  ...  With motion up the chain, where data of higher order is inferred from lower-order data, the crucial question is whether privacy norms governing lower-order data are sufficient for the inferred higher-order  ...  56 Jane Yakowitz, Tragedy of the Data Commons, 25 harv.  ... 
doi:10.1515/til-2019-0008 fatcat:j6mrxxrir5gehbiobufivisoxy

Big data governance of personal health information and challenges to contextual integrity

Jenifer Sunrise Winter, Elizabeth Davidson
2018 The Information Society  
Through a case study of a controversial public-private partnership between Royal Free Trust, a National Health Service hospital system in the United Kingdom, and Alphabet's AI venture DeepMind Health,  ...  we investigate how forms of data governance were adapted, as PHI data flowed into new use contexts, to address concerns of contextual integrity, which is violated when personal information collected in  ...  How can we foster transparency in big data analytics with regards to AI/machine learning and intellectual property constraints?  ... 
doi:10.1080/01972243.2018.1542648 fatcat:imiemcyhmneghdwbmk2hyl6nai

Big Data Management and Analysis for Business Informatics - A Survey

Stéphane Marchand-Maillet, Birgit Hofreiter
2015 Enterprise Modelling and Information Systems Architectures - An International Journal  
We show how the trend of Big Data is related to data security and user privacy. We then investigate automated ways of performing data analysis for Business Intelligence.  ...  We finally review how groups of users may be seen as a workforce in business through the notion of human computation or crowdsourcing, associated with the notions of trust and reputation.  ...  In Zhang et al. (2012) , a review of commercial systems for Visual Analytics, to support facing this big data era is proposed in the context of Business Intelligence.  ... 
doi:10.18417/emisa.9.1.6 dblp:journals/emisaij/Marchand-MailletH14 fatcat:a7ffekoqznegthpf42gbxbzqgu

Artificial intelligence in recommender systems

Qian Zhang, Jie Lu, Yaochu Jin
2020 Complex & Intelligent Systems  
The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.  ...  recommender systems.  ...  Federated learning [180] is able to preserve privacy by sending model parameters to a server instead of storing data in a central server.  ... 
doi:10.1007/s40747-020-00212-w fatcat:ev3cyoy2mjeuhmq3rymkx2shsy

Sketching an AI Marketplace: Tech, Economic, and Regulatory Aspects

Abhishek Kumar, Benjamin Finley, Tristan Braud, Sasu Tarkoma, Pan Hui
2021 IEEE Access  
In this work, we sketch guidelines for a new AI diffusion method based on a decentralized online marketplace.  ...  We consider the technical, economic, and regulatory aspects of such a marketplace including a discussion of solutions for problems in these areas.  ...  For example, only two of them mention that they support scalable privacy-preserving model training paradigms like federated learning.  ... 
doi:10.1109/access.2021.3050929 fatcat:ltxoaxkib5ecdpktueb67vd25u

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

David Gil, Antonio Ferrández, Higinio Mora-Mora, Jesús Peral
2016 Sensors  
Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.  ...  However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it.  ...  Decoupling, privacy preserving, anonymization A framework for privacy-preserving data-as-a-service SOA data mashup [114] Data services, privacy preserving, data integration SOA architecture for high-dimensional  ... 
doi:10.3390/s16071069 pmid:27409623 pmcid:PMC4970116 fatcat:gnajig5ttbbctov5lvx6tlgati
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