Non-intrusive load monitoring (NILM) enables extracting individual appliances’ power consumption data from an aggregated power signal in a cost-effective way. The extracted appliance-level power data can greatly facilitate tasks such as malfunction diagnosis and load forecasting, which are of significant importance for efficient energy use. Various neural networks including convolutional neural network (CNN), recurrent neural network (RNN), and transformer (self-attention-based neural network) are employed in the design of NILM solutions since 2015. In particular, CNN is revealed to extract certain critical features such as power level and typical usage duration, thus, achieving superior performance. However, the global features especially the dependency correlations between different positions in a sequence cannot be properly acquired. Accordingly, we devise a novel model incorporating an added attention layer to overcome this limitation. The added self-attention mechanism can automatically assign attention scores/weights to different features outputted by convolutional layers, which amplifies the positive influence of critical knowledge while realizing global reference. Moreover, this model can explicitly extract the appliance’s multi-state information, which endows the model with more interpretability. We further improve our model by substituting the added self-attention mechanism with a lightweight one, which decreases the number of model parameters while maintaining the decomposing accuracy. Experimental results over two real-world datasets, REDD and UK-DALE, demonstrate that our models outperform the state-of-the-art, achieving 6.5%-52% improvement on average for three standard evaluation metrics. Moreover, we offer an extracted NILM solution system architecture incorporating neural networks, aiming to establish a framework to support future research.

Nonintrusive load monitoring (NILM) has attracted tremendous attention owing to its cost efficiency in electricity and sustainable development. NILM aims at acquiring individual appliance power consumption rates using an aggregated power smart meter reading. Each individual appliance’s power consumption enables users to monitor their electricity usage habits for rational saving strategies. This is also a valuable tool for detecting failure in appliances. However, the major barriers facing NILM schemes are issues of accurately capturing the features of each appliance and decreasing the computing time. Motivated by these challenges, we propose a new, efficient, and accurate NILM scheme, consisting of a learning step and a decomposing step. In the learning step, we propose the Fast Search-and-Find of Density Peaks (FSFDP) clustering algorithm aimed at capturing the features of the power consumption patterns of appliances. In the decomposing step, we propose a Genetic Algorithm (GA)-based matching algorithm to estimate the power consumption of each individual appliance using the aggregated power reading. Using elitist and catastrophic strategies, this step reduces the searching space to achieve considerable efficiency. Experimental results using the Reference Energy Disaggregation Dataset (REDD) indicate that our proposed scheme promotes accuracy by 10% and reduces the decomposing time by half.

Deep Neural Networks (DNNs) have made significant progress in addressing the Non-Intrusive Load Monitoring (NILM) task, which aims to disaggregate appliance-level power signals from aggregated meter readings. Despite these advancements, existing DNN-based NILM approaches rely on training with power signals from individual appliances, which are obtained intrusively through sensor installations. This method is not only expensive but also poses a risk of damaging the original circuits. To overcome these limitations, we introduce the State-based Supervised NILM (SS-NILM) problem. Instead of using appliance power signal labels, we leverage on-off state information, which can be collected in a non-intrusive manner. However, solving SS-NILM presents a challenge, as it requires developing a model that maps on-off state labels to the corresponding appliance power signals in an indirectly supervised setting. In this work, we propose a state-based DNN that predicts the power signals of multiple target appliances simultaneously. The model is trained by minimizing the discrepancy between the aggregated prediction and the true aggregated power signal. Additionally, the model predicts the on-off states of appliances, which are used as auxiliary information to improve the accuracy of power signal predictions. Extensive experiments conducted on real-world datasets demonstrate that our model, trained using non-intrusive on-off state information, achieves performance comparable to that of traditional NILM models.

Text-to-image (T2I) diffusion models enjoy great popularity and many individuals and companies build their applications based on publicly released T2I diffusion models. Previous studies have demonstrated that backdoor attacks can elicit T2I diffusion models to generate unsafe target images through textual triggers. However, existing backdoor attacks typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance of T2I diffusion models. To address these issues, we propose EvilEdit, a training-free and data-free backdoor attack against T2I diffusion models. EvilEdit directly edits the projection matrices in the cross-attention layers to achieve projection alignment between a trigger and the corresponding backdoor target. We preserve the functionality of the backdoored model using a protected whitelist to ensure the semantic of non-trigger words is not accidentally altered by the backdoor. We also propose a visual target attack EvilEdit _VTA, enabling adversaries to use specific images as backdoor targets. We conduct empirical experiments on Stable Diffusion and the results demonstrate that the EvilEdit can backdoor T2I diffusion models within one second with up to 100% success rate. Furthermore, our EvilEdit modifies only 2.2% of the parameters and maintains the model's performance on benign prompts. Our code is available at https://github.com/haowang-cqu/EvilEdit.

Digital forensics is crucial to fight crimes around the world. Decentralized Digital Forensics (DDF) promotes it to another level by channeling the power of blockchain into digital investigations. In this work, we focus on the privacy and security of DDF. Our motivations arise from (1) how to track an anonymous-and-malicious data user who leaks only a part of the previously requested data, (2) how to achieve access control while protecting data from untrusted data centers, and (3) how to enable efficient and secure search on the blockchain. To address these issues, we propose Themis: an anonymous and secure DDF scheme with traceable anonymity, private access control, and efficient search. Our framework is boosted by establishing a Trusted Execution Environment in each authority (blockchain node) for securing the uploading, requesting, and searching. To instantiate the framework, we design a secure and robust watermarking scheme in conjunction with decentralized anonymous authentication, a private and fine-grained access control scheme, and an efficient and secure search scheme based on a dynamically updated data structure. We formally define and prove the privacy and security of Themis. We build a prototype with Ethereum and Intel SGX2 to evaluate its performance, which supports processing data from a considerable number of data providers and investigators.

Bitcoin first proposed the Nakamoto consensus that applies proof of work into the blockchain structure to build a trustless append-only ledger. The Nakamoto consensus solves the distributed consistency problem in the public network but wastes too much computing power. Instead of consuming computing resources, many improved consensus schemes address this problem by leveraging miners’ storage resources. However, these schemes fail to let miners store data constantly and usually rely on a dealer to assign data, which is hard to build a reliable decentralized storage system. In this article, we first design a variant consensus algorithm named Proof of Continuous Work (PoCW) with a storage-related incentive mechanism. Miners can accumulate mining advantage by continuously submitting proofs of storage. Then, we present a hash ring-based data allocation algorithm using the blockchain’s state. Combined with both of them, we build a reliable blockchain-based storage system without relying on any third parties. The theoretical analysis and simulation results demonstrate that the proposed system has higher reliability than those existing systems, and we also give practical suggestions about system parameters. Finally, we discuss additional benefits that our system brings.