Honglu Jiang

• Doctor of Philosophy
• Assistant Professor at Miami University

Federated learning (FL) is a distributed learning framework that allows clients to jointly train a model by uploading parameter updates rather than sharing local data. FL deployed on a client-edge-cloud hierarchical architecture, named Hierarchical Federated Learning (HFL), can accelerate model training and accommodate more clients with reduced com...

In this paper, we investigate a two-user uplink nonorthogonal multiple access (NOMA) short-packet communication system in flat Rayleigh fading channels, which includes a base station, a central user and a cell-edge user. To derive the average block error rate of the system model, unlike the works in the literature where the product term ( $\mathbb...

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violation...

Different from the conventional transmit-then-compute scheme, which has been widely considered in mobile edge computing (MEC) networks, we propose a transmit-while-compute scheme to reduce the average latency. Specifically, by means of the partial offloading technique, the computational task is divided into multiple subtasks which are sequentially...

As one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training power, lowering the social welfare. In this paper, we model the interactions among organi...

Since its launch in 2014, Amazon Echo family of devices has seen a considerable increase in adaptation in consumer homes and offices. With a market worth millions of dollars, Echo is used for diverse tasks such as accessing online information, making phone calls, purchasing items, and controlling the smart home. Echo offers user-friendly voice inte...

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violation...

Although Metaverse has recently been widely studied, its practical application still faces many challenges. One of the severe challenges is the lack of sufficient resources for computing and communication on local devices, resulting in the inability to access the Metaverse services. To address this issue, this paper proposes a practical blockchain-...

As an effective method to protect the daily access to sensitive data against malicious attacks, the audit mechanism has been widely deployed in various practical fields. In order to examine security vulnerabilities and prevent the leakage of sensitive data in a timely manner, the database logging system usually employs an online signaling scheme to...

As an effective method to protect the daily access to sensitive data against malicious attacks, the audit mechanism has been widely deployed in various practical fields. In order to examine security vulnerabilities and prevent the leakage of sensitive data in a timely manner, the database logging system usually employs an online signaling scheme to...

As one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training, lowering the social welfare. In this paper, we model the interactions among organization...

As one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training, lowering the social welfare. In this paper, we model the interactions among organization...

Conventional private data publication mechanisms aim to retain as much data utility as possible while ensuring sufficient privacy protection on sensitive data. Such data publication schemes implicitly assume that all data analysts and users have the same data access privilege levels. However, it is not applicable for the scenario that data users of...

Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential...

Conventional private data publication mechanisms aim to retain as much data utility as possible while ensuring sufficient privacy protection on sensitive data. Such data publication schemes implicitly assume that all data analysts and users have the same data access privilege levels. However, it is not applicable for the scenario that data users of...

Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential...

Social networks have gained tremendous popularity recently. Millions of people use social network apps to share precious moments with friends and family. Users are often asked to provide personal information such as name, gender, and address when using social networks. However, as the social network data are collected, analyzed, and re-published at...

For the Social Internet of Things (SIoT), the interaction among ever increasing number of smart devices results in an exponential increase of services, which leads to an extreme difficulty for users to find suitable services. To address this issue, most existing recommendation algorithms are based on the data stored on the centralized server and di...

Online social networks have gained tremendous popularity and have dramatically changed the way we communicate in recent years. However, the publishing of social network data raises more and more privacy concerns. To protect user privacy, social networking data are usually anonymized before being released. Nevertheless, existing anonymization techni...

Differential privacy provides strong privacy preservation guarantee in information sharing. As social network analysis has been enjoying many applications, it opens a new arena for applications of differential privacy. This article presents a comprehensive survey connecting the basic principles of differential privacy and applications in social net...

Differential privacy is effective in sharing information and preserving privacy with a strong guarantee. As social network analysis has been extensively adopted in many applications, it opens a new arena for the application of differential privacy. In this article, we provide a comprehensive survey connecting the basic principles of differential pr...

Online Social Networks (OSNs) have transformed the way that people socialize. However, when OSNs bring people convenience, privacy leakages become a growing worldwide problem. Although several anonymization approaches are proposed to protect information of user identities and social relationships, existing de-anonymization techniques have proved th...

Online social networks provide platforms for people to interact with each other and share moments of their daily life. The online social network data are valuable for both academic and business studies, and are usually processed by anonymization methods before being published to third parties. However, several existing de-anonymization techniques c...

Social network data is widely shared, forwarded and published to third parties, which led to the risks of privacy disclosure. Even thought the network provider always perturbs the data before publishing it, attackers can still recover anonymous data according to the collected auxiliary information. In this paper, we transform the problem of de-anon...

The MAC (medium access control) of CSMA (carrier sense multiple access) is widely used in distributed wireless networks with random node locations. In CSMA MAC, two nodes that are within the range of one another cannot transmit packets simultaneously. Modeling the concurrently transmitting nodes is crucial for the performance analysis of the CSMA n...

The proliferation of ubiquitous Internet and mobile devices has brought about the exponential growth of individual data in big data era. The network user data has been confronted with serious privacy concerns for extracting valuable information during the process of data mining. Differential privacy preservation is a new paradigm independent of the...

With the access of smart phones to cell networks, the requirements of the coverage and the average spectrum efficiency are more and more important. And using the limited resources to improve the performance of the network is challengeable. In this paper, we use the poisson cluster process to model the multi-input multi-output (MIMO) heterogeneous c...

The application and deployment of wireless Ad hoc networks provided great conveniences for people. Meanwhile, it also brought great challenges. One of the most important issues is privacy preservation, especially the protection of user’s sensitive information in the process of access control. This paper puts forward the idea of multi-factor authent...

The data of social networks contains a large amount of personal information, of which may be public or insignificant, but some may be sensitive and private. Once the user privacy leaked, it may bring a variety of troubles for users. Holders of social networking data first conduct anonymization before the data is published. However, the simple anony...

The leader election problem is one of the fundamental problems in distributed computing. Different from most of the existing results studying the multi-leader election in static networks or one leader election in dynamic networks, in this paper, we focus on the multi-leader election in dynamic sensor networks where nodes are deployed randomly. A ce...

In the Internet of Things (IoT), various battery-powered wireless devices are connected to collect and exchange data, and typical traffic is periodic and heterogeneous. Polling with power management is a very promising technique that can be used for communication among these devices in the IoT. In this paper, we propose a novel and scalable model t...

In Carrier Sense Multiple Access (CSMA) media access control (MAC), two nodes that are within the range of one another can not simultaneously transmit packets. Modeling the concurrently transmitting nodes is the key to analyzing the performance of a CSMA network. In this paper, we study the density of concurrently transmitting nodes and propose a M...

The characteristics of wireless networks present formidable challenges to the study of broadcasting problem. A crucial issue in wireless networks is the energy consumption, because of the nonlinear attenuation properties of radio signals. Another crucial issue is the trade-off between reaching more nodes in a single hop by using higher power versus...