Jian Weng

Jinan University · Department of Computer Science

As a fundamental technology of the Metaverse, blockchain enables numerous Metaverse applications. However, the blockchain consensus mechanism’s high energy consumption and performance bottlenecks have become an impediment to the green and sustainable development of the Metaverse. To tackle this challenge, we propose a lightweight consensus service...

There is substantial attention to federated learning with its ability to train a powerful global model collaboratively while protecting data privacy. Despite its many advantages, federated learning is vulnerable to backdoor attacks, where an adversary injects malicious weights into the global model, making the global model's targeted predictions in...

Federated learning, as a form of distributed learning, aims to protect local data while utilizing distributed data to train a global model. However, federated learning still faces challenges related to privacy leakage in Internet of Things(IoTs). Researches indicate that server can infer private information from local gradients. Additionally, malic...

Proxy re-encryption (PRE) is a cryptosystem that realizes efficient encrypted data sharing by allowing a third party proxy to transform a ciphertext intended for a delegator (i.e., Alice) to a ciphertext intended for a delegatee (i.e., Bob). Attribute-based proxy re-encrypftion (AB-PRE) generalizes PRE to the attribute-based scenarios, enabling fin...

Autonomous vehicles (AVs) rely on controller area network (CAN), which ensures the communication between massive electronic control units (ECUs) and passenger safety. Although CAN is a lightweight and reliable broadcast protocol, its vulnerability has caused CAN to confront serious security threats. Adversaries and malicious organizations can impai...

Federated learning has shown great potential in Internet of Things (IoTs) for performing intelligent decision making. It allows IoT devices to collaboratively train a neural network upon the data they collect while separately keeping these data staying local. However, several research works have shown that such architecture still faces security cha...

The “Right to be Forgotten” rule in machine learning (ML) practice enables some individual data to be deleted from a trained model, as pursued by recently developed machine unlearning techniques. To truly comply with the rule, a natural and necessary step is to verify if the individual data are indeed deleted after unlearning. Yet, previous parame...

Deep neural networks (DNNs) have been widely used in the field of synthetic aperture radar (SAR) image classification, but they also face increasingly serious threats from various malicious attacks, among which the backdoor attack is a common threat. The defense methods against backdoor attacks are extremely important. Although the defense methods...

Deep neural networks (DNNs) have been widely applied to the synthetic aperture radar (SAR) images detection and classification recently while different kinds of adversarial attacks from malicious adversary and the hidden vulnerability of DNNs may lead to serious security threats. The state-of-the-art DNNs-based SAR image detection models are design...

Anonymous Payment Channel Hub (PCH), one of the most promising layer-two solutions, settles the scalability issue in blockchain while guaranteeing the unlinkability of transacting parties. However, such developments bring conflicting requirements, i.e., hiding the sender-to-receiver relationships from any third party but opening the relationship to...

The development of unmanned aerial vehicle (UAV) technology has been advancing rapidly and is widely applied in various domains. Compared to a single UAV, the multi-UAV system, known as the UAV Ad Hoc Network (UANET), can collaborate to accomplish complex tasks more efficiently. Due to the UAVs communicating through open wireless channels, they are...

Few of proposed protocols that aim to secure in-vehicle networks consider how to generate group key between Electronic Control Units (ECUs) for message authentication or encryption. A key exchange protocol, which provides higher security and satisfies limited power of ECUs, is indispensable with the development of intelligent connected vehicle. In...

The traveling salesman problem (TSP) is one of the classic combinatorial optimization problems, which can be widely used in intelligent transportation and logistics field. Neural network has shown great potential in combinatorial optimization tasks. However, it faces privacy leakage when a TSP neural combinatorial optimization network and user's da...

Copyright protection, including copyright registration, copyright transfer and infringement penalty, plays a critical role in preventing illegal usage of original works. The mainstream traditional copyright protection schemes need an authority online all the time to handle copyright issues and face some problems such as intricate copyright transfer...

Private Set Intersection (PSI) enables two parties to learn the intersection of their input sets without exposing other items that are not within the intersection. However, real-world applications often require more complex computations than just obtaining the intersection. In this paper, we consider the setting where each item in the input set has...

Autonomous path proxy re-encryption (AP-PRE) is a type of PRE that implements control on the delegation path in a multi-hop PRE. AP-PRE forces the proxy to perform the transformation along a predefined path without revealing the underlying plaintext. There are several applications of AP-PRE, including electronic medical systems, data sharing, and e...

Industrial control systems (ICSs) are facing serious and evolving security threats because of a variety of malicious attacks. Deep learning-based intrusion detection systems (IDSs) have been widely considered as one of promising security solutions for ICSs, but these deep neural networks for IDSs in ICSs have been designed manually, which are extre...

Abstract In recent years, deep learning has been applied to a variety of scenarios in Industrial Internet of Things (IIoT), including enhancing the security of IIoT. However, the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the designers. The authors have made the first contr...

We propose a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing in a distrustful multi-stakeholder environment. The approach is aimed to enable that a CNN model developer convinces a user of the truthful CNN performance over non-public data from multiple testers , while respecting model and data priv...

Federated learning (FL) trains a model over multiple datasets by collecting the local models rather than raw data, which can help facilitate distributed data analysis in many real-world applications. Since the model parameters can leak information about the training datasets, it is necessary to preserve the privacy of the FL participants' local mod...

Presently, similar sequence search is a fundamental technique in genomic data research. Patients or researchers, who want to check whether they or their research objects have genetic diseases or potential illnesses, need to query similar sequences with their genes in certain genomic databases. As a consequence, this may raise privacy issues since t...

In an acceleration-based Secure Device Pairing (SDP) scheme, two unauthenticated devices continuously measure their own acceleration. If the similarity of their measurements are sufficiently high, the devices will build a secure communication channel assume that it is hard for any attacker to estimate their measurements in real-time. This paper dem...

In recent years, cyber-physical systems (CPS) have been widely deployed in industrial manufacturing fields and our daily living domains. End–end–edge collaboration, coupling mobile edge computing and device-to-device communication, is a promising computation paradigm to meet the stringent real-time demands of large-scale CPS applications. However,...

With the prevalence of outsourced computation, such as Machine Learning as a Service, protecting the privacy of sensitive data throughout the whole computation is a critical yet challenging task. The problem becomes even more tricky when multiple sources of input and/or multiple recipients of output are involved, who would encrypt/decrypt data usin...

As a typical application of mobile crowdsourcing, streaming media has been attracting increasing attention since recent years. However, traditional streaming media platforms, such as Netflix, Disney+, and Hulu, may suffer some problems like inflexible billing modes, lacking sustainability in the incentive mechanisms, and management censorship. Thes...

Ateniese et al. introduced the primitive of matchmaking encryption (ME) at CRYPTO 2019 and left open several important questions, which include extending ME to fuzzy cases or giving an efficient ME in the identity-based setting without relying on random oracles. The main challenge is to achieve fuzzy bilateral access control while providing identit...

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the prese...

As the deployment of blockchains expands across various industries, the demand for exchanging digital assets among blockchain users has risen. Most of existing solutions either solely support asset exchanges among users on the same blockchain, or have limitations by only enabling cross-chain asset exchanges among a few specific blockchains or requi...

Blockchain rewriting has become widely explored for addressing data deletion requirements, such as error data deletion, space-saving, and compliance with the “right-to-be-forgotten” rule. However, existing approaches are inadequate for handling cross-chain redaction issues, in facing with the increasing need for inter-chain communication. In partic...

In recent years, serverless edge computing has been widely employed in the deployments of Internet-of-things (IoT) applications. Despite considerable research efforts in this field, existing works fail to jointly consider essential factors such as energy, reliability, personalized user requirements, and stochastic application executions. This overs...

Acting as an important part of Internet of Things (IOTs), Vehicular Ad-hoc Network (VANET) has attracted considerable attention in recent years, where the emergency reporting system is a significant branch and can improve road safety and optimize traffic management. In an emergency reporting system, the authenticity of the emergency messages needs...

Data trading in the Internet of Things (IoT) based on off-chain payment has attracted a lot of attention recently since it can significantly improve transaction throughput and reduce transaction fees compared with traditional blockchain-based solutions that trade data only with on-chain transactions. However, the vulnerability of off-chain payment...

With the increasing awareness of user privacy protection and communication security, encrypted traffic has increased dramatically. Usually utilizing the flow information of the traffic, flow statistics-based methods are able to classify encrypted traffic. However, these methods require a large number of packets and manual selection of statistical f...

Sidechain-based Cross-chain exchange protocols enable payers to exchange cryptocurrencies among different blockchains via a sidechain. Many efforts, such as P2DEX (ACNS' 21), have been proposed to enhance cross-chain exchange privacy protection. However, existing sidechain-based cross-chain solutions for Monero on privacy concerns have limitations:...

In this article, we proposed an equivalent formulation of the k-winners-take-all (k-WTA) problem as a constrained optimization problem by including the Laplacian matrix of the undirected connected communication graph to adapt to the distributed computing scenario, where an additional auxiliary variable is introduced. To solve the optimization probl...

Finding dynamic Moore-Penrose inverses (DMPIs) in real-time is a challenging problem due to the time-varying nature of the inverse. Traditional numerical methods for static Moore-Penrose inverse are not efficient for calculating DMPIs and are restricted by serial processing. The current state-of-the-art method for finding DMPIs is called the zeroin...

In secure machine learning inference, most current schemes assume that the server is semi-honest and honestly follows the protocol but attempts to infer additional information. However, in real-world scenarios, the server may behave maliciously, e.g., using low-quality model parameters as inputs or deviating from the protocol. Although a few studie...

Mobile cloud storage (MCS) provides clients with convenient cloud storage service. In this article, we propose an efficient, secure and privacy-preserving mobile cloud storage scheme, which protects the data confidentiality and privacy simultaneously, especially the access pattern. Specifically, we propose an oblivious selection and update (OSU) pr...

Fully homomorphic signature schemes in identity-based settings can provide authenticity, homomorphism, and non-repudiation as do traditional digital signatures, while simplifying the public key infrastructure (PKI) requirements, in which each user in the system can use his or her identity as a public key. As identity-based systems (IBS) have a natu...

This paper proposes a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing, enabling a CNN model developer to convince a user of the truthful CNN performance over non-public data from multiple testers, while respecting model privacy. To balance the security and efficiency issues, three new efforts are done b...

Encrypted image retrieval is a promising technique for achieving data confidentiality and searchability the in cloud-assisted Internet of Things (IoT) environment. However, most of the existing top- $k$ ranked image retrieval solutions have low retrieval efficiency and may leak the values and orders of similarity scores to the cloud server. Hence,...

Searchable encryption(SE) allows users to efficiently retrieve data over encrypted cloud data, but most existing SE schemes only support exact keyword search, resulting in false results due to minor typos or format inconsistencies of queried keywords. The fuzzy keyword search can avoid this limitation, but still incurs low search accuracy and effic...

To ensure the security of outsourced data without affecting data availability, one can use Symmetric Searchable Encryption (SSE) to achieve search over encrypted data. Considering that query users may search with misspelled words, the fuzzy search should be supported. However, conventional privacy-preserving fuzzy multi-keyword search schemes are i...

Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users ( e.g., avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users’ location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain sati...

With increasingly popular GPS-equipped mobile devices (e.g., smartphones, tablets, laptops), massive spatio-textual data has been outsourced to cloud servers for storage and analysis such as spatial keyword search. However, existing privacy-preserving spatial keyword query schemes only support coarse-grained non-temporal access control in single us...

Genome-Wide Association Study (GWAS) aims at detecting the association between diseases and Single-Nucleotide Polymorphisms (SNPs) with statistical techniques and has great potential for disease diagnosis. To obtain high-quality results, GWAS requires large-scale genomic data containing individuals' privacy information. Thus, how to improve the eff...

Location-based routing is a widely adopted message transmission mechanism in Vehicular Ad Hoc Networks (VANETs). While the existing location-based routing schemes of VANETs ignore the location privacy protection of vehicles, leading that the drivers to be tracked, and further threaten the safety of their life and property. To address the above issu...

With the growing popularity of the Internet-of-Things (IoT), a massive amount of purpose-specific, heterogeneous, inexpensive devices have been deployed. To allow these devices to perform their duties and collaborate efficiently, designing a secure and dependable communication channel is necessary. Pairing, as the fundamental procedure for establis...

Attribute-based conditional proxy re-encryption (AB-CPRE) allows delegators to carry out attribute-based control on the delegation of decryption by setting policies and attribute vectors. The fine-grained control of AB-CPRE makes it suitable for a variety of applications, such as cloud storage and distributed file systems. However, all existing AB-...

With the increasing number of traffic accidents and terrorist attacks by modern vehicles, vehicular digital forensics (VDF) has gained significant attention in identifying evidence from the related digital devices. Ensuring the law enforcement agency to accurately integrate various kinds of data is a crucial point to determine the facts. However, m...

Identity-based encryption with equality test (IBEET), derived from public key encryption with equality test (PKEET), allows the equality test algorithm on two ciphertexts without decrypting the messages and simplifies the certificate management of PKEET. In response to the explosive growth of quantum computing, recently, some IBEET schemes based on...

With the increasing connectivity between the Electronic Control Units (ECUs) and the outside world, safety and security have become stringent problems. The Controller Area Network (CAN) bus is the most commonly used in-vehicle network protocol, which lacks security mechanisms by design, so it is vulnerable to various attacks. In this paper, we prop...

In recent years, the exploration on large-scale cyber-physical systems (CPSs) has become a fertile research field of significant impact. Large-scale CPS applications cover not only manufacturing and production areas but also daily living domains. Traditional solutions dedicated for large-scale CPSs mainly concentrate on the service latency or relia...

Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. However, the DL models may be prone to the membership inference attack, where an attacker determines whether a gi...

To ensure the security of images outsourced to the malicious cloud without affecting searchability on such outsourced (typically encrypted) images, one could use privacy-preserving Content-Based Image Retrieval (CBIR) primitive. However, conventional privacy-preserving CBIR schemes based on Searchable Symmetric Encryption (SSE) are not capable of s...

A traditional neural network cannot realize the invariance of image rotation and distortion well, so an attacker can fool the neural network by adding tiny disturbances to an image. If traffic signs are attacked, automatic driving will probably be misguided, leading to disastrous consequences. Inspired by the principle of human vision, this paper p...

Dropout is one of the most widely used methods to avoid overfitting neural networks. However, it rigidly and randomly activates neurons according to a fixed probability, which is not consistent with the activation mode of neurons in the human cerebral cortex. Inspired by gene theory and the activation mechanism of brain neurons, we propose a more i...

Vehicular networks have tremendous potential to improve road safety, traffic efficiency, and driving comfort, where cooperative vehicular safety applications are a significant branch. In cooperative vehicular safety applications, through the distributed data fusion for large amounts of data from multiple nearby vehicles, each vehicle can intelligen...

Vehicular networks have tremendous potential to improve the road safety and traffic efficiency, and the adoption of the Space-Air-Ground Integrated Network (SAGIN) architecture in vehicular networks can greatly improve the performance of vehicular networks by leveraging the respective advantages of the space, air, and ground segments on coverage, f...

When authenticating a group of RFID tags, a common method is to authenticate each tag with some challenge-response exchanges. However, sequentially authenticating individual tags one by one might not be desirable, especially when considering that a reader often has to deal with multiple tags within a limited period, since it will incur long scannin...

Industrial automation and control systems (IACS) are tremendously employing supervisory control and data acquisition (SCADA) network. However, their integration into IACS is vulnerable to various cyber-attacks. In this paper, we firstly present population extremal optimization (PEO)-based deep belief network detection method (PEO-DBN) to detect the...

In recent years, the advance in information technology has promoted a wide span of emerging cyber‐physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality‐of...

In this article, we propose a novel learning and near-optimal control approach for underactuated surface (USV) vessels with unknown mismatched periodic external disturbances and unknown hydrodynamic parameters. Given a prior knowledge of the periods of the disturbances, an analytical near-optimal control law is derived through the approximation of...

The development of Industrial Internet of Things (IIoT) provides massive abundant data resources for trading and mining. However, the existing data trading schemes achieve data usage control at the cost of high latency, thereby resulting in poor service quality as the values of IIoT data degrade over time. This article proposes a monitor-based usag...

As a potential application field of the 6th Generation (6G) communication technology and a promising part of massive Internet of Things (IoT), vehicular networks have attracted considerable attention from both academia and industry in recent years, where the cooperative safety applications are a significant branch. It is widely acknowledged that th...

The measurement algebraic connectivity plays an important role in many graph theory-based investigations, such as cooperative control of multiagent systems. In general, the measurement is considered to be centralized. In this article, a distributed model is proposed to estimate the algebraic connectivity (i.e., the second smallest eigenvalue of the...

Secure and efficient access authentication is one of the most important security requirements for vehicular networks, but it is difficult to fulfill due to potential security attacks and long authentication delay caused by high vehicle mobility, etc. Most of the existing authentication protocols, either do not consider attacks like single point of...

In certificateless proxy signature (CLPS), the key generation center is responsible for initializing the system parameters and can obtain the opportunity to adaptively set some trapdoors in them when wanting to launch some attacks. Until now, how to withstand the malicious-but-passive key generation center (MKGC) attacks in CLPS is still an interes...

The great development of smart networks enables Internet of Vehicles (IoV) as a promising paradigm to provide pervasive services, where privacy issues for location-based services (LBSs) have attracted considerable attention. In terms of location privacy, inspired by differential privacy, geo-indistinguishability (Geo-Ind) has recently become a prev...

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent medical model, which includes illness states, treatment methods...

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment...

Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. However, the DL models may be prone to the membership inference attack, where an attacker determines whether a gi...

Load frequency control (LFC) is widely employed to keep smart grids stable and secure. This paper proposes an adaptive resilient LFC scheme for sub-systems of smart grids under denial-of-service (DoS) attacks with energy constraint. Firstly, a resilient triggering communication scheme is introduced, where the triggering condition includes the uncer...

The popularity of Internet-of-Things (IoT) comes with security concerns. Attacks against wireless communication venues of IoT (e.g., Man-in-the-Middle attacks) have grown at an alarming rate over the past decade. Pairing, which allows the establishment of the secure communicating channels for IoT devices without a prior relationship, is thus a para...

Mobile edge computing (MEC) is an emerging technology to transform the cloud-based computing services into the edge-based ones. Autonomous vehicular network (AVNET), as one of the most promising applications of MEC, can feature edge learning and communication techniques, improving the safety for autonomous vehicles (AVs). This paper focuses on the...

In 2014, a new security definition of a revocable identity-based signature (RIBS) with signing key exposure resistance was introduced. Based on this new definition, many scalable RIBS schemes with signing key exposure resistance were proposed. However, the security of these schemes is based on traditional complexity assumption, which is not secure...

Lattice reduction is a popular preprocessing strategy in multiple-input multiple-output (MIMO) detection. In a quest for developing a low-complexity reduction algorithm for large-scale problems, this paper investigates a new framework called sequential reduction (SR), which aims to reduce the lengths of all basis vectors. The performance upper boun...

Deep learning can achieve higher accuracy than traditional machine learning algorithms in a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn tremendous attention from information security community, in which neither training data nor the training model is expected to be exposed. Federated learning is a popular...