Many tracking companies collect user data and sell it to data markets and advertisers. While they claim to protect user privacy by anonymizing the data, our research reveals that significant privacy risks persist even with anonymized data. Attackers can exploit this data to identify users' accounts on other websites and perform targeted identity alignment. In this paper, we propose an effective identity alignment scheme for accurately identifying targeted users. We develop a data collector to obtain the necessary datasets, an algorithm for identity alignment, and, based on this, construct two types of de-anonymization attacks: thepassive attack, which analyzes tracker data to align identities, and theactive attack, which induces users to interact online, leading to higher success rates. Furthermore, we introduce, for the first time, a novel evaluation framework for online tracking-based identity alignment. We investigate the key factors influencing the effectiveness of identity alignment. Additionally, we provide an independent assessment of our generated dataset and present a fully functional system prototype applied to a cryptocurrency use case.

Artificial Intelligence Generated Content (AIGC) has created a fertile ground for image steganography. Existing Coverless Image Steganography (CIS) methods rely on image semantics to encode secrets, transmitting stego images without embedding, inherently resisting steganalysis. However, constructing CIS Datasets (CISDs) for these methods demands excessive resources, making them impractical for communication. Moreover, achieving low cost and high security is unattainable under these conditions. Therefore, we propose a CIS method based on semantic-controlled text-to-image generation. Our method disguises users as typical AIGC community members utilizing mainstream black-box text-to-image generation with Stable Diffusion (SD). During pre-processing, plain prompts, derived from dialogues with a large language model, are divided into coded and uncoded prompts through our encryption process, where a secret key determines coded prompts. In communication, confusion prompts are selected from uncoded and coded prompts, excluding those determined by secrets. Subsequently, our stego shuffling process combines topic, secret, and confusion prompts to produce stego prompt sets. Diverse stego images maintaining visual topic consistency are generated from these sets using SD with generation seeds indicating transmission order. By introducing confusion prompts, our method is secure from recognition when revealing stego prompts. Experimental results demonstrate our method achieves low communication costs and enhances communication security.

Image steganalysis has always been an important topic in the field of information security, and researchers have designed many excellent steganalysis models. However, the existing steganalysis models tend to construct a single path and increase the convolution kernels to reduce the size of feature maps, which is not comprehensive enough to extract the features and may boost the number of parameters. In addition, the single residual block stacking may pay attention to protecting stego signals and neglect the mining of hidden features. To address these issues, we propose a steganalysis model based on dual-path enhancement and fractal downsampling, which is suitable for both spatial and JPEG domains. The model reuses and strengthens noise residuals through two dual-path enhancement blocks, and designs a fractal downsampling block for downsampling at multiple levels, angles, and composition structures. The experimental results demonstrate that the proposed model achieves the best detection performance in both spatial and JPEG domains compared with other start-of-the-art methods. Besides, we design a series of ablation experiments to verify the rationality of each component.

Breast cancer is a highly heterogeneous disease with varied subtypes, prognoses and therapeutic responsiveness. Human leukocyte antigen class I (HLA-I) shapes the immunity and thereby influences the outcome of breast cancer. However, the implications of HLA-I variations in breast cancer remain poorly understood. In this study, we established a multiomics cohort of 1156 Chinese breast cancer patients for HLA-I investigation. We calculated four important HLA-I indicators in each individual, including HLA-I expression level, somatic HLA-I loss of heterozygosity (LOH), HLA-I evolutionary divergence (HED) and peptide-binding promiscuity (Pr). Then, we evaluated their distribution and prognostic significance in breast cancer subtypes. We found that the four breast cancer subtypes had distinct features of HLA-I indicators. Increased expression of HLA-I and LOH were enriched in triple-negative breast cancer (TNBC), while Pr was relatively higher in hot tumors within TNBCs. In particular, a higher Pr indicated a better prognosis in TNBCs by regulating the infiltration of immune cells and the expression of immune molecules. Using the matched genomic and transcriptomic data, we found that mismatch repair deficiency-related mutational signature and pathways were enriched in low-Pr TNBCs, suggesting that targeting mismatch repair deficiency for synthetic lethality might be promising therapy for these patients. In conclusion, we presented an overview of HLA-I indicators in breast cancer and provided hints for precision treatment for low-Pr TNBCs.

Digital image have become the main source of human information acquisition and exchange, which is widely used in aerospace, biomedical and military fields. Therefore, to ensure the secure transmission of digital image, this paper proposes a secure spatio-temporal chaotic pseudorandom generator for image encryption is proposed. Firstly, we consider the potential impact of precision loss in digital circuits on the degradation of chaotic systems. Therefore, we employ the unscented Kalman filter (UKF) to assess accuracy loss in both Logistic, Sine and Chebyshev maps, which is compensated for by introducing perturbations into the spatio-temporal chaotic system. Secondly, we design new Sine maps and Chebyshev maps with time-varying delays to perturb the time dimension of the non-adjacent coupled lattice and improve the complexity and security of the chaotic system. In the end, we use the newly designed spatio-temporal chaotic system as a pseudo-random generator to design a new image encryption scheme. In this paper, we present a security proof for the newly proposed spatio-temporal chaotic system and image encryption scheme. Furthermore, security experiments demonstrate that the spatiotemporal chaotic system and image encryption scheme presented in this paper exhibit improved uniform distribution, absence of chaos degradation or predictability issues while offering randomness suitable for engineering applications.

With the rapid development of Internet of Things (IoT), big data analytics (BDA) has gradually stepped into the spotlight of IoT research. However, existing research mostly focused on improving the efficiency of data mining, the wishes and profits of the participants in BDA are largely ignored. In real systems, it is always supposed that the IoT data owners accept the big data analytics in IoT (BDA-IoT), the theoretical modeling of BDA-IoT is needed to be constructed urgently. In this paper, aiming to provide theoretical modeling of the practical application of BDA-IoT, we prove the feasibility of participants voluntarily participating in BDA-IoT for the first time. Subsequently, a non-cooperative game theory model with incentive and payment mechanisms is constructed, and the multi-parties interaction process in BDA-IoT is simulated. The overall benefits of all participants are then discussed, the rationality and feasibility of our study are proved. Simulation results show the feasibility of our model in improving the benefits of all stakeholders in a non-cooperative game, and the best choice for all participants is to accept BDA-IoT.

Abstract 3D acquisition of archaeological artefacts has become an essential part of cultural heritage research for preservation or restoration purpose. Statues, in particular, have been at the center of many projects. In this paper, we introduce a way to improve the understanding of acquired statues representing real or imaginary creatures by registering a simple and pliable articulated model to the raw point set data. Our approach performs a Forward And bacKward Iterative Registration (FAKIR) which proceeds joint by joint, needing only a few iterations to converge. We are thus able to detect the pose and elementary anatomy of sculptures, with possibly non realistic body proportions. By adapting our simple skeleton, our method can work on animals and imaginary creatures.

Deep learning has brought a breakthrough to the performance of speech recognition. The speech recognition systems based on deep neural networks have obtained the state-of-the-art performance on various speech recognition tasks. These systems almost utilize the Mel-frequency cepstral coefficients or the Mel-scale log-filterbank coefficients, which are based on short-time Fourier transform. Although these features are designed based on the auditory characteristics of the human, it is a problem that the inherent tradeoff of the temporal and frequency resolution still exists in spectral representations based on short-time Fourier transform. In this paper, we propose a multi-scale method to mitigate the tradeoff and a model architecture that enables to analyze speech at multiple scale. Experiments are conducted on TIMIT and HKUST corpus. We compare the proposed multi-scale features and traditional features at various number of configurations. Experimental results show that the proposed model architecture can obtain significant performance improvement.