Securing Smart Grid In-Network Aggregation through False Data Detection

Existing prevention-based secure in-network data aggregation schemes for the smart grids cannot e ectively detect accidental errors and falsified data injected by malfunctioning or compromised meters. In this work, we develop a light-weight anomaly detector based on kernel density estimator to locate the smart meter from which the falsified data is injected. To reduce the overhead at the collector, we design a dynamic grouping scheme, which divides meters into multiple interconnected groups and distributes the verification and detection load among the root of the groups. To enable outlier detection at the root of the groups, we also design a novel data re-encryption scheme based on bilinear mapping so that data previously encrypted using the aggregation key is transformed in a form that can be recovered by the outlier detectors using a temporary re-encryption key. Therefore, our proposed detection scheme is compatible with existing in-network aggregation approaches based on additive homomorphic encryption. We analyze the security and eÿciency of our scheme in terms of storage, computation and communication overhead, and evaluate the performance of our outlier detector with experiments using real-world smart meter consumption data. The results show that the performance of the light-weight detector yield high precision and recall

Enabling Privacy-Preserving and Publicly Auditable Federated Learning

Federated learning (FL) has attracted widespread attention because it supports the joint training of models by multiple participants without moving private dataset. However, there are still many security issues in FL that deserve discussion. In this paper, we consider three major issues: 1) how to ensure that the training process can be publicly audited by any third party; 2) how to avoid the influence of malicious participants on training; 3) how to ensure that private gradients and models are not leaked to third parties. Many solutions have been proposed to address these issues, while solving the above three problems simultaneously is seldom considered. In this paper, we propose a publicly auditable and privacy-preserving federated learning scheme that is resistant to malicious participants uploading gradients with wrong directions and enables anyone to audit and verify the correctness of the training process. In particular, we design a robust aggregation algorithm capable of detecting gradients with wrong directions from malicious participants. Then, we design a random vector generation algorithm and combine it with zero sharing and blockchain technologies to make the joint training process publicly auditable, meaning anyone can verify the correctness of the training. Finally, we conduct a series of experiments, and the experimental results show that the model generated by the protocol is comparable in accuracy to the original FL approach while keeping security advantages.Comment: ICC 2024 - 2024 IEEE International Conference on Communications Conference Progra

UPOCR: Towards Unified Pixel-Level OCR Interface

In recent years, the optical character recognition (OCR) field has been proliferating with plentiful cutting-edge approaches for a wide spectrum of tasks. However, these approaches are task-specifically designed with divergent paradigms, architectures, and training strategies, which significantly increases the complexity of research and maintenance and hinders the fast deployment in applications. To this end, we propose UPOCR, a simple-yet-effective generalist model for Unified Pixel-level OCR interface. Specifically, the UPOCR unifies the paradigm of diverse OCR tasks as image-to-image transformation and the architecture as a vision Transformer (ViT)-based encoder-decoder. Learnable task prompts are introduced to push the general feature representations extracted by the encoder toward task-specific spaces, endowing the decoder with task awareness. Moreover, the model training is uniformly aimed at minimizing the discrepancy between the generated and ground-truth images regardless of the inhomogeneity among tasks. Experiments are conducted on three pixel-level OCR tasks including text removal, text segmentation, and tampered text detection. Without bells and whistles, the experimental results showcase that the proposed method can simultaneously achieve state-of-the-art performance on three tasks with a unified single model, which provides valuable strategies and insights for future research on generalist OCR models. Code will be publicly available

Novel Sm3+-Mn4+ co-activated CaGdMgNbO6 red phosphor with anomalous thermal quenching for plant growth lighting

Red inorganic phosphors are widely utilized in the field of artificial plant supplemental lighting. Nevertheless, their red light emission falls short of satisfying the plant's dual requirements for both deep-red and far-red light simultaneously. In this work, multiple red emission Sm3+ and Sm3+-Mn4+ activated CaGdMgNbO6 (CGMNO) red phosphors were prepared by high-temperature solid-state reaction. The luminescence characteristics, thermal stability, energy transfer behaviour and optical temperature sensing properties were investigated. The 550–750 nm emission bands of CGMNO:Sm3+ and CGMNO:Sm3+, Mn4+ matched the absorption band of the plant. The inhibition of energy transfer between activated ions and the structural reconstruction of conduction band traps reveal the anomalous thermal quenching behavior of Sm3+ ions at high temperatures. These results show that Sm3+-Mn4+ co-activated CGMNO phosphors have potential applications in plant growth

Emodin Induced SREBP1-Dependent and SREBP1-Independent Apoptosis in Hepatocellular Carcinoma Cells

Reynoutria multiflora (Thunb.) Moldenke (He Shou Wu) has been used for about 20 centuries as a Chinese medicinal herb for its activities of anticancer, anti-hyperlipidemia, and anti-aging. Previously, we found that He Shou Wu ethanol extract could induce apoptosis in hepatocellular carcinoma cells, and we also screened its active components. In this study, we investigated whether lowering lipid metabolism of emodin, a main active component in He Shou Wu, was associated with inhibitory effects in hepatocellular carcinoma cells. The correlation of apoptosis induction and lipid metabolism was investigated. The intrinsic apoptotic cell death, lipid production, and their signaling pathways were investigated in emodin-treated human hepatocellular carcinoma cells Bel-7402. The data showed that emodin triggered apoptosis in Bel-7402 cells. The mitochondrial membrane potential (ΔΨm) was reduced in emodin-treated Bel-7402 cells. We also found that emodin activated the expression of intrinsic apoptosis signaling pathway-related proteins, cleaved-caspase 9 and 3, Apaf 1, cytochrome c (CYTC), apoptosis-inducing factor, endonuclease G, Bax, and Bcl-2. Furthermore, the level of triglycerides and desaturation of fatty acids was reduced in Bel-7402 cells when exposed to emodin. Furthermore, the expression level of messenger RNA (mRNA) and protein of sterol regulatory element binding protein 1 (SREBP1) as well as its downstream signaling pathway and the synthesis and the desaturation of fatty acid metabolism-associated proteins (adenosine triphosphate citrate lyase, acetyl-CoA carboxylase alpha, fatty acid synthase (FASN), and stearoyl-CoA desaturase D) were also decreased. Notably, knock-out of SREBP1 in Bel-7402 cells was also found to induce less intrinsic apoptosis than did emodin. In conclusion, these results indicated that emodin could induce apoptosis in an SREBP1-dependent and SREBP1-independent manner in hepatocellular carcinoma cells

ZnO/Cu₂O heterojunction integrated fiber-optic biosensor for remote detection of cysteine

Indium tin oxide, semiconductor nanomaterial ZnO, and Cu2O were first loaded on the surface of the optical fiber to form an optical fiber probe. Large-volume macroscopic spatial light is replaced by an optical fiber path, and remote light injection is implemented. Based on the optical fiber probe, a photoelectrochemical biosensor was constructed and remote detection of cysteine was realized. In this tiny device, the optical fiber probe not only acts as a working electrode to react with the analyte but also directs the light exactly where it is needed. Simultaneously, the electrochemical behavior of cysteine on the surface of the working electrode is dominated by diffusion-control, which provides strong support for quantitative detection. Then, under the bias potential of 0 V, the linear range of the fiber-optic-based cysteine biosensor was 0.01∼1 μM, the regression coefficient (R2) value was 0.9943. In spiked synthetic urine, the detection of cysteine was also realized by the integrated biosensor. Moreover, benefiting from the low optical fiber loss, the new structure also possesses a unique remote detection function. This work confirms that photoelectrochemical biosensors can be integrated via optical fibers and retain comparable sensing performance. Based on this property, different materials can also be loaded on the surface of the optical fiber for remote detection of other analytes. It is expected to facilitate the research on fiber-optic-based integrated biosensors and show application prospects in diverse fields such as biochemical analysis and disease diagnosis.</p

Self-powered optical fiber biosensor integrated with enzymes for non-invasive glucose sensing

To alleviate the discomfort associated with frequent blood glucose detection in diabetic patients, a novel non-invasive tear glucose biosensor has been developed. This involved the design and preparation of a photoelectrochemical probe based on an optical fiber and biological enzymes. One end of the optical fiber connects to a light source, acting as an energy source and imparting, self-powered capability to the biosensor. The opposite end is loaded with nanomaterials and glucose oxidase, designed for insertion into the sample to realize photoelectrochemical sensing. This innovative configuration not only improves the integration of the biosensor but is also suitable for analyzing minuscule voluminal samples. The results show that the proposed biosensor exhibits a linear range from 10 nM to 100 μM, possesses a low detection limit of 4.1 nM and a short response time of 0.7 s. Benefiting from the high selectivity of the enzyme, the proposed biosensor demonstrates excellent resistance to the interference of common tear components. In summary, this work provides a more effective method for non-invasive glucose detection and affords valuable ideas for the design and fabrication of non-invasive and self-powered biosensors