One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i.e., latent prints or fingermarks). Despite the success of fixed-length embeddings for rolled and slap fingerprint recognition, the features learned for latent fingerprint matching have mostly been limited to local

Federated learning (FL) is an emerging paradigm of privacy-preserving distributed machine learning that effectively deals with the privacy leakage problem by utilizing cryptographic primitives. However, how to prevent poisoning attacks in distributed situations has recently become a major FL concern. Indeed, an adversary can manipulate multiple edge nodes and submit malicious gradients to disturb the

A Blockchain network consists of a distributed ledger and a set of nodes participating in the network. The consistency in the ledger’s state is maintained by a Blockchain consensus mechanism. This property is essential since it is assumed that the participants in the network do not trust each other. Most Blockchain consensus algorithms are unsuitable for resource-constrained nodes due to their power

With the explosive growth of Internet traffic, large sensitive and valuable information is at risk of cyber attacks, which are mostly preceded by network reconnaissance. A moving target defense technique called host address mutation (HAM) helps facing network reconnaissance. However, there still exist several fundamental problems in HAM: 1) current approaches cannot be self-adaptive to adversarial

In this paper, we investigate the secure transmission of multi-resource heterogeneous radio access networks (RANs) in space-air-ground integrated network (SAGIN) from the perspective of physical layer security. Considering the network heterogeneity, resource constrain, and channel similarity, it is challenging to implement the physical layer security in SAGIN. Particularly, digital twin (DT) is considered

Facial age estimation aims to rank the face aging data by taking in the correlation among age categories. Conventional age estimation models are trained based on assumed high-quality training annotations in a totally-supervised manner. However, noisy data in a sparse distribution collected from unconstrained environment usually account for the corruption of produced gradients and ordinal relationships

Multi-label pedestrian attribute recognition (PAR) involves assigning multiple attributes to pedestrian images captured by video surveillance cameras. Despite its importance, learning robust attribute-related features for PAR remains a challenge due to the large intra-attribute variations in the image space. These variations, which stem from changes in pedestrian poses, illumination conditions, and

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 identity privacy of the sender and receiver, message confidentiality

Deep Neural Networks (DNNs) are vulnerable to adversarial patch attacks, which raises security concerns for face recognition systems using DNNs. Previous attack methods focus on the perturbation texture and generate adversarial patches with fixed shapes at random or pre-designed locations, which causes poor adversarial transferability. This paper proposes a Spatial Mutable Adversarial Patch (SMAP)

In face antispoofing, it is desirable to have multimodal images to demonstrate liveness cues from various perspectives. However, in most face recognition scenarios, only a single modality, namely visible lighting (VIS) facial images is available. This paper first investigates the possibility of generating polarized (Polar) images from VIS cameras without changing the existing recognition devices to

Trace alignment can improve the subsequent side-channel analysis against the trace. Most current trace alignment schemes are, however, typically operated under a high signal-to-noise ratio (SNR), which demands them to be noise reduced before alignment when practical applications in the complex environment. In this paper, we propose a novel strategy for applying adaptive filtering in trace alignment

Otoacoustic emission (OAE) biometrics are inherently robust to replay and falsification attacks. The widely studied transient-evoked OAE (TEOAE) is non-stationary and offers biometric value only in normal-hearing individuals since it is more susceptible to hearing loss. To address these issues, this paper presents a novel yet promising OAE biometric modality-stimulus-frequency OAE (SFOAE). Unlike TEOAE

Fuzzy message detection is a novel cryptographic primitive in which the remote storage cloud can help the client carry out fuzzy detections with some false-positive rate. This primitive protects the privacy of clients and does not reveal exactly the matching messages to the untrusted cloud. However, the existing public-key-based schemes require many public keys to generate a single flag ciphertext

Cyber-physical systems are becoming more intelligent with the adoption of heterogeneous sensor networks and machine learning capabilities that deal with an increasing amount of input data. While this complexity aims to solve problems in various domains, it adds new challenges for the system assurance. One issue is the rise in the number of abnormal behaviors that affect system performance due to possible

Unconstrained Palmprint Recognition (UPR) shows engaging potential owing to its high hygiene and privacy. The unconstrained acquisition usually produces wide variations, against which deep methods resort to large samples that are unavailable in practice, however. We focus on Few-Shot UPR (FS-UPR), a more general problem, recognizing query samples given a few support samples per class. Because scarce

Mobile crowdsensing (MCS) systems typically struggle to address the challenge of data aggregation, incentive design, and privacy protection, simultaneously. However, existing solutions usually focus on one or, at most, two of these issues. To this end, this paper presents CROWD FA, a novel paradigm for privacy-preserving MCS through federated analytics (FA), which aims to achieve a well-rounded solution

Most person re-identification (re-ID) approaches are based on representation learning of pedestrian images, which assume that the person’s appearance captured by cameras in the target is fully available. However, the exposure of appearance could cause serious privacy leakages. To address this issue, we focus on a new privacy-protected person re-ID task where the person’s appearance is unavailable for

The primary objective of anti-jamming techniques is to ensure that the transmitted data arrives at the intended receiver without being disturbed or jammed with by any jamming signal or other hostile activities to ensuring the security of the communication system. Deep reinforcement learning (DRL) has been extensively utilized in solving the dynamic spectrum anti-jamming problem. However, most of existing

Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible adversarial perturbations. As a complementary type of adversary, patch attacks that introduce perceptible perturbations to the images have attracted the interest of researchers. Existing patch attacks rely on the architecture of the model or the probabilities of predictions and perform poorly in the decision-based

Wireless energy harvesting (WEH) enabled Device-to-Device (D2D) communication emerges as an effective technique to improve spectral and energy efficiencies. However, D2D users are usually power-constrained devices that may be monitored or attacked by adversaries easily. To confuse randomly distributed adversaries in WEH-enabled D2D underlaying cellular networks, power beacons (PBs) can be used to send

Secret-key agreement using physical identifiers is a promising security protocol for the authentication of users and devices with small chips, owing to its lightweight security. In the previous studies, the fundamental limits of such a protocol were analyzed, and the results showed that two auxiliary random variables were involved in the capacity region expressions. However, with two auxiliary random

Industrial Internet of Things (IIoT), which has the capability of perception, monitoring, communication and decision-making, has already exposed more security problems that are easy to be invaded by malware because of many simple edge devices that help smart factories, smart cities and smart homes. In this paper, a two-layer malware spread−patch model $IIPV$ is proposed based on a hybrid patches distribution

The rapid growth of electric vehicle (EV) penetration has led to more flexible and reliable vehicle-to-grid-enabled cyber-physical systems (V2G-CPSs). However, the increasing system complexity also makes them more vulnerable to cyber-physical threats. Coordinated cyber attacks (CCAs) have emerged as a major concern, requiring effective detection and mitigation strategies within V2G-CPSs. Digital twin

Deep neural networks have been widely used in various domains owing to the success of deep learning. However, recent studies have shown that these models are vulnerable to adversarial examples, leading to inaccurate predictions. In this paper, we focus on the issue of adversarial robustness by examining it through the lens of semantic information, which drives us to propose a new perspective, i.e.

It has become a trend for clients to outsource their encrypted databases to remote servers and then leverage the Searchable Encryption technique to perform secure data retrieval. However, the method has yet to be considered a crucial need for replication on searchable encrypted data. It calls for challenging works on Dynamic Searchable Symmetric Encryption (DSSE) since clients must share the search

Proxy Re-Encryption (PRE), a special cryptographic primitive, can efficiently perform ciphertext conversion on the cloud. To enable the data owner (i.e. delegator) to authorize a file access path according to the different priorities of the users (i.e. delegatees), autonomous path proxy re-encryption (AP-PRE) was proposed, where the delegator can generate a proxy re-encryption autonomous path in order

The framework of differential privacy protects an individual’s privacy while publishing query responses on congregated data. In this work, a new noise addition mechanism for differential privacy is introduced where the noise added is sampled from a hybrid density that resembles Laplace in the centre and Gaussian in the tail. With a sharper centre and light, sub-Gaussian tail, this density has the best

Despite the myriad benefits offered by blockchain technology, most of them still face several interrelated issues, such as limited transaction throughput, exorbitant transaction fees, and protracted confirmation times. Payment channel networks have emerged as a promising scalability solution, allowing two mutually distrustful users to engage in multiple off-chain transactions. However, existing schemes

Palmprint has gained popularity as a biometric modality and has recently attracted significant research interest. The competition-based method is the prevailing approach for hand-crafted palmprint recognition, thanks to its powerful discriminative ability to identify distinctive features. However, the competition mechanism possesses vast untapped advantages that have yet to be fully explored. In this

Secure cloud-assisted data publish/subscribe (Pub/Sub) service provides an asynchronous method for publishers and subscribers to non-interactively exchange encrypted messages. Besides performing conjunctive subscription policy, numerous data Pub/Sub systems have recently been proposed to provide dynamic access control enforced from the publisher side to the subscriber side. However, these solutions

With the spread of tampered images, locating the tampered regions in digital images has drawn increasing attention. The existing tampering localization methods, however, suffer from severe performance degradation when the images are subjected to some post-processing, as the tampering traces would be distorted by the post-processing operations. The poor robustness against post-processing has become

Local differential privacy (LDP) is a promising technique to realize privacy-preserving data aggregation without a trusted aggregator. Normally, an LDP protocol requires each user to locally perturb his raw data and submit the perturbed data to the aggregator. Consequently, LDP is vulnerable to output poisoning attacks. Malicious users can skip the perturbation and submit carefully crafted data to

Cloud computing has been widely accepted as a computing paradigm to offer high-quality data services on demand. However, it suffers from various attacks as the cloud service provider and data owners are not in the same trusted domain. To support data confidentiality, existing cloud-based systems apply cryptographic tools to issue the decryption key to data users to share data in a controlled way. However

Intelligent false data injection on load measurements can trigger false relay operation (FRO) of frequency-based protection relays, affecting the power system frequency and thus threatening the security of power systems. In this paper, we propose an optimization-based formal model to find the optimal false data injection attack (OFDIA) with the minimum required time leading to an FRO. The proposed

Deep hiding, embedding images with others using deep neural networks, has demonstrated impressive efficacy in increasing the message capacity and robustness of secret sharing. In this paper, we challenge the robustness of existing deep hiding schemes by preventing the recovery of secret images, building on our in-depth study of state-of-the-art deep hiding schemes and their vulnerabilities. Leveraging

Data management services provided by the public cloud can economize the enterprise’s local storage costs, and meanwhile, realize data sharing among the enterprise. Corporate users, however, usually have concerns about the security and privacy of their data stored in the public cloud. Searchable encryption has been used as a secure method for enterprise users in the public cloud to share data via keyword

The One-Tap Authentication (OTAuth) based on the cellular network is a password-less login service provided by Mobile Network Operator (MNO) through the unique communication gateway access technique. The service allows app users to quickly sign up or log in with their mobile phone numbers without entering a password. Due to its convenience, OTAuth has been widely used by various apps. However, some

Researchers analyze underground forums to study abuse and cybercrime activities. Due to the size of the forums and the domain expertise required to identify criminal discussions, most approaches employ supervised machine learning techniques to automatically classify the posts of interest. Human annotation is costly. How to select samples to annotate that account for the structure of the forum? We present

Deep neural network (DNN) based website fingerprinting (WF) attacks pose a severe threat to the privacy of Tor users. To overcome this challenge, adversarial perturbation based WF defenses have been recently proposed to fool the classifiers of attackers, through purposefully perturbing the user’s traffic traces. Unfortunately, these defenses significantly deteriorate once the WF attacks are enhanced

Deep neural network (DNN) models have been widely used in many tasks due to their superior performance. However, DNN models are usually vulnerable to adversarial example attacks, which limits their applications in many safety-critic scenarios. How to effectively detect adversarial examples to enhance the robustness of DNN models has attracted much attention in recent years. Most adversarial example

The revelations about massive surveillance have created significant interest in algorithm substitution attack (ASA), where an honest implementation of a cryptographic primitive is replaced by a subverted one which can help “big brother” to break cryptographic security while generating output indistinguishable from the honest output. The current known ASAs on public-key cryptography are either dedicated

The semi-supervised face recognition problem has become a popular research topic in recent years. However, one common and important situation, in which the unlabeled data is shallow, has rarely been considered in most existing works. In this paper, shallow data means there are only few images per identity. In the unlabeled shallow situation, the existing semi-supervised face recognition methods generally

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables multiple clients to train a unified model without disclosing their private data. However, susceptibility to membership inference attacks (MIAs) arises due to the natural inclination of FL models to overfit on the training data during the training process, thereby enabling MIAs to exploit the subtle differences in

Recent studies have shown that side-information estimation (SIE) via JPEG image deblocking/restoration is effective in enhancing steganographic security when the side-information of JPEG rounding errors is unavailable. The polarity map of deblocking errors can work well in modulating handcrafted embedding costs. However, it may not be easy to design an optimal deblocking method that is universal to

Latent graph structure and stimulus of graph-structured data contain critical private information, such as brain disorders in functional magnetic resonance imaging data, and can be exploited to identify individuals. It is critical to perturb the latent information while maintaining the utility of the data, which, unfortunately, has never been addressed. This paper presents a novel approach to obfuscating

Existing Android malware detection methods are usually hard to simultaneously resist various obfuscation techniques. Therefore, bytecode-based code obfuscation becomes an effective means to circumvent Android malware analysis. Building obfuscation-resilient Android malware analysis methods is a challenging task, due to the fact that various obfuscation techniques have vastly different effects on code

Federated learning (FL) has achieved state-of-the-art performance in distributed learning tasks with privacy requirements. However, it has been discovered that FL is vulnerable to adversarial attacks. The typical gradient inversion attacks primarily focus on attempting to obtain the client’s private input in a white-box manner, where the adversary is assumed to be either the client or the server. However

Real-time eyeblink detection in the wild is a recently emerged challenging task that suffers from dramatic variations in face attribute, pose, illumination, camera view and distance, etc. One key issue is to well characterize eyelid’s intrinsic motion (i.e., approaching and departure between upper and lower eyelid) robustly, under unconstrained conditions. Towards this, a novel eyelid’s intrinsic motion-aware

Federated learning (FL) is a privacy-aware data mining strategy keeping the private data on the owners’ machine and thereby confidential. The clients compute local models and send them to an aggregator which computes a global model. In hybrid FL, the local parameters are additionally masked using secure aggregation, such that only the global aggregated statistics become available in clear text, not

The widespread adoption of cloud computing and the exponential growth of data highlight the need for secure data sharing and querying. Attribute-based keyword search (ABKS) has emerged as an efficient means of searching encrypted data stored in the cloud. However, existing ABKS schemes incur high end-to-end delay and are vulnerable to quantum computer attacks and/or (insider) keyword guessing attacks

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 using different keys. Considering many computation tasks

Contactless palmprint recognition, an emerging biometric technology, has attracted increasing attention due to its noninvasive and high practicability characteristics. Although it is naturally suitable for mobile application scenarios, the following two challenges severely limit its recognition performance: 1) the inconsistency in acquisition devices used in training and testing, and 2) many subjects

Recent advances in the fingerprinting of deep neural networks are able to detect specific instances of models, placed in a black-box interaction scheme. Inputs used by the fingerprinting protocols are specifically crafted for each precise model to be checked for. While efficient in such a scenario, this nevertheless results in a lack of guarantee after a mere modification of a model (e.g. finetuning

Privacy image classification (PIC) has attracted increasing attention as it can help people make appropriate privacy decisions when sharing images. Most recently, some pioneer research efforts have been made to utilize multimodal information for PIC, since multi-modality can provide richer information than single modality. Those research efforts on multimodal PIC are under the assumption of independently

In online lending services, fraud prediction is an especially critical step to control loss risk and improve processing efficiency. Unfortunately, it is definitely challenging since the ex-ante prediction actually needs to be made only based on the most basic information of applicants. This work figures out that the essential difficulty here is the low information density of data associations which

An inner product (IP) argument is a proof system that convinces the verifier of an IP relation between committed integer vectors. IP arguments are crucial building blocks for range proof and zero knowledge arguments, which can be applied to verifiable computation, confidential transactions, decentralized identification, and so on. This paper proposes a novel efficient IP argument with a trustless setup

Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and benign traffic. Compared with signature-based methods, they have higher scalability and flexibility. However, affected by the frequent variants and updates of malware

The large number of devices characterizing Wireless Sensor Networks (WSNs) provide the benefits of observing, tracking, and recording everything; nonetheless, the cumulative computing power of those devices is typically not utilized, and the few implementations taking advantage of it neglect privacy or are application-specific. This manuscript describes a privacy-preserving protocol that enables WSN

Although great progress has been sparked in video anomaly detection (VAD) by deep neural networks (DNNs), existing solutions still fall short in two aspects: 1) The extraction of video events cannot be both precise and comprehensive. 2) The semantics and temporal context are under-explored. To tackle above issues, we are inspired by cloze tests in language education and propose a novel approach named

Against spatial memory violations threatening a vast amount of legacy software, various safety solutions have been suggested for decades. However, their practical uses have been impeded by diverse reasons, such as significant overheads and mandatory modifications of existing architectures. Accordingly, there has been a clear need for a practical safety solution that is fast enough and yet runs on commodity