A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data. A considerable number of studies on data pricing can be found in literature. This article attempts to comprehensively review the state-of-the-art on existing data pricing studies to provide a general understanding of this emerging research area. Our key contribution lies in a new taxonomy of data pricing studies that unifies different attributes determining data prices. The basis of our framework categorises these studies by the kind of market structure, be it sell-side, buy-side, or two-sided. Then in a sell-side market, the studies are further divided by query type, which defines the way a data consumer accesses data, while in a buy-side market, the studies are divided according to privacy notion, which defines the way to quantify privacy of data owners. In a two-sided market, both privacy notion and query type are used as criteria. We systematically examine the studies falling into each category in our taxonomy. Lastly, we discuss gaps within the existing research and define future research directions.

Over recent decades, machine learning has significantly advanced network communication, enabling improved decision-making, user behavior analysis, and fault detection. Simultaneously, the growth of communication networks has facilitated the efficient collection of large-scale training data. Traditional centralized machine learning, however, requires collecting data from users, raising significant concerns about privacy and security. Decentralized approaches, where participants exchange computation results instead of raw private data, mitigate these risks but introduce challenges related to trust and verifiability. A critical issue arises: How can one ensure the integrity and validity of computation results shared by other participants? Existing survey articles predominantly address security and privacy concerns in decentralized machine learning, whereas this survey uniquely highlights the emerging issue of verifiability. Recognizing the critical role of zero-knowledge proofs in ensuring verifiability, we present a comprehensive review of Zero-Knowledge Proof-based Verifiable Machine Learning (ZKP-VML). To clarify the research problem, we present a definition of ZKP-VML consisting of four algorithms and several key security properties. In addition, we provide an overview of the current research landscape by systematically organizing the research timeline and categorizing existing schemes based on their security properties. Furthermore, through an in-depth analysis of each existing scheme, we summarize their technical contributions and optimization strategies, aiming to uncover common design principles underlying ZKP-VML schemes. Building on the reviews and analysis presented, we identify current research challenges and suggest future research directions. To the best of our knowledge, this is the most comprehensive survey to date on verifiable decentralized machine learning and ZKP-VML.

Driver identification faces various challenges in real-time applications. These challenges include high dimensional input data, moderate accuracy, scalability issues and need for custom-built in-vehicle sensors. The conventional biometric solutions are either less accurate, unscalable, or costly for practical application. In those solutions, high accuracy comes at the cost of preservation of privacy. Similarly, scalability and cost-effectiveness are inversely proportional to one another. This paper proposes a driver identification scheme to pare these challenges. The scheme uses data from the global positioning system (GPS) to learn an individual's driving pattern, which is commonly deployed in in-car navigation systems and can also be found in general purpose hand-held devices in the prevailing market. The proposed scheme is innovative in terms of providing a single solution for the existing challenges. It is more practical and scalable with large numbers of the drivers also being cost-effective and accurate, which makes it applicable in real-time applications with least overhead costs for different resources. The consequent analysis and empirical results show that the scheme could identify drivers with significant accuracy given only GPS data. To assess the scheme in depth, we perform experiments both on the collected data and the real-world open datasets. The average accuracy approximates above 96% for up to 25 drivers.

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.

The Ethereum cryptocurrency system becomes prevalent with the increasing number of daily transactions. It is a high-quality channel to secretly transfer information because senders and recipients are pseudonymous and transactions are tamper-resistant. Several existing covert channels have been designed for several years. However, most of the existing covert channels can either be detected by their special patterns or have low embedding rate. In this paper, we first use the VALUE field of a transaction in the Ethereum system to construct a HMAC-based multiple-bit embedding (HMAC-based MBE) scheme. We further proposed a Hash-based multiple-bit embedding (Hash-based MBE) scheme to enhance the covertness. Theoretical and experimental analysis show that both schemes have either higher embedding rate or better covertness than that of the existing works.

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.