{"title":"Call for papers: Special issue on privacy-preserving data mining for artificial intelligence of things","authors":"","doi":"10.26599/BDMA.2021.9020026","DOIUrl":null,"url":null,"abstract":"Artificial Intelligence of Things (AIoT) is experiencing unimaginable fast booming with the popularization of end devices and advanced machine learning and data processing techniques. An increasing volume of data is being collected every single second to enable Artificial Intelligence (AI) on the Internet of Things (IoT). The explosion of data brings significant benefits to various intelligent industries to provide predictive services and research institutes to advance human knowledge in data-intensive fields. To make the best use of the collected data, various data mining techniques have been deployed to extract data patterns. In classic scenarios, the data collected from IoT devices is directly sent to cloud servers for processing using diverse methods such as training machine learning models. However, the network between cloud servers and massive end devices may not be stable due to irregular bursts of traffic, weather, etc. Therefore, autonomous data mining that is self-organized by a group of local devices to maintain ongoing and robust AI services plays a growing important role for critical IoT infrastructures. Privacy issues become more concerning in this scenario. The data transmitted via autonomous networks are publicly accessible by all internal participants, which increases the risk of exposure. Besides, data mining techniques may reveal sensitive information from the collected data. Various attacks, such as inference attacks, are emerging and evolving to breach sensitive data due to its great financial benefits. Motivated by this, it is essential to devise novel privacy-preserving autonomous data mining solutions for AIoT. In this Special Issue, we aim to gather state-of-art advances in privacy-preserving data mining and autonomous data processing solutions for AIoT. Topics include, but are not limited to, the following: • Privacy-preserving federated learning for AIoT • Differentially private machine learning for AIoT • Personalized privacy-preserving data mining • Decentralized machine learning paradigms for autonomous data mining using blockchain • AI-enhanced edge data mining for AIoT • AI and blockchain empowered privacy-preserving big data analytics for AIoT • Anomaly detection and inference attack defense for AIoT • Privacy protection measurement metrics • Zero trust architectures for privacy protection management • Privacy protection data mining and analysis via blockchain-enabled digital twin.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":"5 1","pages":"80-80"},"PeriodicalIF":7.7000,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8254253/9663253/09663263.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data Mining and Analytics","FirstCategoryId":"1093","ListUrlMain":"https://ieeexplore.ieee.org/document/9663263/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial Intelligence of Things (AIoT) is experiencing unimaginable fast booming with the popularization of end devices and advanced machine learning and data processing techniques. An increasing volume of data is being collected every single second to enable Artificial Intelligence (AI) on the Internet of Things (IoT). The explosion of data brings significant benefits to various intelligent industries to provide predictive services and research institutes to advance human knowledge in data-intensive fields. To make the best use of the collected data, various data mining techniques have been deployed to extract data patterns. In classic scenarios, the data collected from IoT devices is directly sent to cloud servers for processing using diverse methods such as training machine learning models. However, the network between cloud servers and massive end devices may not be stable due to irregular bursts of traffic, weather, etc. Therefore, autonomous data mining that is self-organized by a group of local devices to maintain ongoing and robust AI services plays a growing important role for critical IoT infrastructures. Privacy issues become more concerning in this scenario. The data transmitted via autonomous networks are publicly accessible by all internal participants, which increases the risk of exposure. Besides, data mining techniques may reveal sensitive information from the collected data. Various attacks, such as inference attacks, are emerging and evolving to breach sensitive data due to its great financial benefits. Motivated by this, it is essential to devise novel privacy-preserving autonomous data mining solutions for AIoT. In this Special Issue, we aim to gather state-of-art advances in privacy-preserving data mining and autonomous data processing solutions for AIoT. Topics include, but are not limited to, the following: • Privacy-preserving federated learning for AIoT • Differentially private machine learning for AIoT • Personalized privacy-preserving data mining • Decentralized machine learning paradigms for autonomous data mining using blockchain • AI-enhanced edge data mining for AIoT • AI and blockchain empowered privacy-preserving big data analytics for AIoT • Anomaly detection and inference attack defense for AIoT • Privacy protection measurement metrics • Zero trust architectures for privacy protection management • Privacy protection data mining and analysis via blockchain-enabled digital twin.
期刊介绍:
Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge.
Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications.
Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more.
With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.