A5072627238- Privacy-Preserving Technologies in Data
- Mobile Crowdsensing and Crowdsourcing
- Energy Efficient Wireless Sensor Networks
- Mobile Ad Hoc Networks
- IoT and Edge/Fog Computing
- Privacy, Security, and Data Protection
- Energy Harvesting in Wireless Networks
- Complex Network Analysis Techniques
- Advanced MIMO Systems Optimization
- Opportunistic and Delay-Tolerant Networks
- Data Management and Algorithms
- Cognitive Radio Networks and Spectrum Sensing
- Cryptography and Data Security
- Genetics, Bioinformatics, and Biomedical Research
- Advanced Graph Neural Networks
- Blockchain Technology Applications and Security
- Caching and Content Delivery
- Cooperative Communication and Network Coding
- Bioinformatics and Genomic Networks
- Human Mobility and Location-Based Analysis
- Adversarial Robustness in Machine Learning
- Gene expression and cancer classification
- Vehicular Ad Hoc Networks (VANETs)
- Age of Information Optimization
- Advanced Image and Video Retrieval Techniques
Georgia State University
2016-2025
Tsinghua University
2016-2025
Nanjing University of Aeronautics and Astronautics
2025
Commercial Aircraft Corporation of China (China)
2025
Nanjing University of Chinese Medicine
2024
Guangdong University of Finance
2024
Fuzhou University
2024
China University of Mining and Technology
2022-2024
Intel (United States)
2021-2024
Jinan University
2023
To provide fine-grained access to different dimensions of the physical world, data uploading in smart cyber-physical systems suffers novel challenges on both energy conservation and privacy preservation. It is always critical for participants consume as little possible uploading. However, simply pursuing efficiency may lead extreme disclosure private information, especially when uploaded contents from are more informative than ever. In this article, we propose a mechanism systems, which...
Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, sensitive information may be predicted out of released through mining techniques. Therefore, sanitizing prior to release is necessary. In this paper, we explore how launch an inference attack exploiting networks with a mixture non-sensitive attributes relationships. We map issue collective classification problem propose model. our model, attacker...
The effective physical data sharing has been facilitating the functionality of Industrial IoTs, which is believed to be one primary basis for Industry 4.0. These data, while providing pivotal information multiple components a production system, also bring in severe privacy issues both workers and manufacturers, thus aggravating challenges sharing. Current designs tend simplify behaviors participants better theoretical analysis, they cannot properly handle IIoTs where are more complicated...
Data privacy arises as one of the most important concerns, facing pervasive commoditization big data statistic analysis in Internet Things (IoT). Current solutions are incapable to thoroughly solve issues on pricing and guarantee utility outputs. Therefore, this paper studies problem trading private results for IoT data, by considering three factors. Specifically, a novel framework range counting is proposed. The applies sampling-based method generate approximated results, which further...
With the popularity of mobile devices, social networks (MSNs) have become an important platform for information dissemination. However, spread rumors in MSNs present a massive threat. Currently, there are two kinds methods to address this: blocking at influential users and spreading truth clarify rumors. most existing works either overlook cost various or only consider different individually. This paper proposes heterogeneous-network-based epidemic model that incorporates describe rumor...
Smart mobile devices and apps have been rolling out at swift speeds over the last decade, turning these into convenient general-purpose computing platforms. Sensory data from smart are important resources to nourish services, they regarded as innocuous information that can be obtained without user permissions. In this article, we show seemingly could cause serious privacy issues. First, demonstrate users' tap positions on screens of identified based sensory by employing some deep learning...
Due to the prominent development of public transportation systems, taxi flows could nowadays work as a reasonable reference trend urban population. Being aware this knowledge will significantly benefit regular individuals, city planners, and companies themselves. However, mindlessly publish such contents severely threaten private information companies. Both their own market ratios sensitive passengers drivers be revealed. Consequently, we propose in paper novel framework for...
Blockchain, a promising decentralized para-digm, can be exploited not only to overcome the shortcomings of traditional crowdsourcing systems, but also bring technical innovations, such as decentralization and accountability. Nevertheless, some critical inherent limitations blockchain have been rarely addressed in literature when it is incorporated into crowdsourcing, which may yield performance bottleneck systems. To further leverage superiority combining this article, we propose an...
Smart IoT systems can integrate knowledge from the surrounding environment, and they are critical components of next-generation Internet. Such usually collect data various dimensions via numerous devices, collected linkable. This means that be combined to derive abundant valuable knowledge. However, may also accessed by malicious third parties reveal sensitive information. In this article, we investigate privacy issues linkable in smart systems, which have not been thoroughly studied...
The ubiquity of devices in Internet Things (IoT) has opened up a large source for IoT data. Machine learning (ML) models with big data is beneficial to our daily life monitoring air condition, pollution, climate change, etc. However, centralized conventional ML rely on all clients' at central server, which seriously threatens user privacy. Federated (FL) emerges as promising solution aiming protect privacy by enabling model training corpus decentralized recent studies indicate FL suffers...
The novel coronavirus, COVID-19, has caused a crisis that affects all segments of the population. As knowledge and understanding COVID-19 evolve, an appropriate response plan for this pandemic is considered one most effective methods controlling spread virus. Recent studies indicate city Digital Twin (DT) beneficial tackling health crisis, because it can construct virtual replica to simulate factors, such as climate conditions, policies, people's trajectories, help efficient inclusive...
Metaverse describes a new shape of cyberspace and has become hot-trending word since 2021. There are many explanations about what Meterverse is attempts to provide formal standard or definition Metaverse. However, these definitions could hardly reach universal acceptance. Rather than providing the Metaverse, we list four must-have characteristics Metaverse: socialization, immersive interaction, real world-building, expandability. These not only carve into novel fantastic digital world, but...
Under the needs of processing huge amounts data, providing high-quality service, and protecting user privacy in artificial intelligence things (AIoT), federated learning (FL) has been treated as a promising technique to facilitate distributed with protection. Although importance developing privacy-preserving FL attracted lot attentions, existing research only focuses on independent identically (i.i.d.) data lacks study non-i.i.d. scenario. What is worse, assumption i.i.d. impractical,...
The Internet of Things (IoT) is penetrating many aspects our daily life with the proliferation artificial intelligence applications. Federated learning (FL) has emerged as a promising paradigm enabling intelligent IoT applications; however, transmitted model gradients or weights still encode private information, which can be exploited to launch inference attacks. One popular way apply local differential privacy (LDP) into FL. However, existing work does not provide practical solution due two...
Abstract Background Microarray data analysis is notorious for involving a huge number of genes compared to relatively small samples. Gene selection detect the most significantly differentially expressed under different conditions, and it has been central research focus. In general, better gene method can improve performance classification significantly. One difficulties in that numbers samples conditions vary lot. Results Two novel methods are proposed this paper, which not affected by...
The amount of sensory data manifests an explosive growth due to the increasing popularity Wireless Sensor Networks (WSNs). scale in many applications has already exceeded several petabytes annually, which is beyond computation and transmission capabilities conventional WSNs. On other hand, information carried by big high redundancy because strong correlation among data. In this paper, we introduce novel concept ϵ-Kernel Dataset, only a small subset can represent vast with loss rate being...
Social network data can help with obtaining valuable insight into social behaviors and revealing the underlying benefits. New big technologies are emerging to make it easier discover meaningful information from market analysis counterterrorism. Unfortunately, both diverse datasets raise stringent privacy concerns. Adversaries launch inference attacks predict sensitive latent information, which is unwilling be published by users. Therefore, there a tradeoff between benefits In this paper, we...
Online social networks have gained significant popularity recently. The problem of influence maximization in online has been extensively studied. However, prior works, propagation the physical world, which is also an indispensable factor, not considered. Location-Based Social Networks (LBSNs) are a special kind people can share location-embedded information. In this paper, we make use mobile crowdsourced data obtained from location-based network services to study LBSNs. A novel model and...
Most existing query processing algorithms for wireless sensor networks (WSNs) can only deal with discrete values. However, since the monitored environment always changes continuously time, values cannot describe accurately and, hence, may not satisfy a variety of requirements, such as queries maximal, minimal, and inflection points. It is, therefore, great interest to introduce new capable time-continuous data. This paper investigates curve WSNs is an effective way represent continuous...
Mobile edge computing (MEC) is a new approach in which computation tasks carried by mobile devices (MDs) can be offloaded to MEC servers or computed locally. Since the MDs are always battery limited and have strict deadlines, how schedule execution of each task energy effectively important. Comparing with existing works, we consider much more complexed scenario, multiple moving sharing heterogeneous servers, problem named as minimum consumption deadline-aware system formulated. Such proved...
Human infections of H5N1 highly pathogenic avian influenza virus have continued to occur in China without corresponding outbreaks poultry, and there is little conclusive evidence the source these infections. Seeking identify human infections, we sequenced 31 viruses isolated from humans (2005 2010). We found a number viral genotypes, not all which similar known counterparts. Guided by patient questionnaire data, also obtained environmental samples live poultry markets dwellings frequented...