ABSTRACT Currently, sharing digital content significantly enhances the value of data, and data purchasing serves as a means of realizing this value after sharing. After a data purchase transaction occurs, although the ownership of the data has been successfully transferred, this process introduces two major challenges: first, maintaining the continuity of cloud data integrity verification after ownership transfers, and second, enabling full‐lifecycle traceability of data to clarify copyright attribution. To address these issues, this paper proposes a public auditing scheme that supports both cloud data ownership transfer and data traceability. First, the proposed scheme introduces an update factor as a mathematical structure to enable the update of HVT (homomorphic verifiable tag) on the cloud. Meanwhile, the CS (cloud service) performs tag update computations, thereby ensuring security while reducing the computational and communication overhead for local users. Furthermore, to achieve transparency throughout the data's lifecycle, copyright information of digital content is embedded into blockchain transactions. A data traceability strategy is then designed leveraging a chameleon hash function, and a detailed traceability process is presented. Security analysis demonstrates that our scheme satisfies correctness and reliability, and a series of comparative experiments further validate its feasibility and efficiency in practical applications.

In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demonstrate a good ability to estimate accurate confidence scores from candidate labels, have been prevalent to deal with PML problems. However, we observe that existing graph-based PML methods typically adopt linear multi-label classifiers and thus fail to achieve superior performance. In this work, we attempt to remove several obstacles for extending them to deep models and propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN). Specifically, we introduce the instance-level and label-level similarities to recover label confidences as well as exploit label dependencies. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels; then, we train the deep model to fit the numerical labels. Moreover, we provide a careful analysis of the risk functions to guarantee the robustness of the proposed model. Extensive experiments on various synthetic datasets and three real-world PML datasets demonstrate that PLAIN achieves significantly superior results to state-of-the-art methods.

Smart meters are deployed in the smart grid to achieve bidirectional communication, the control center can monitor, predict energy consumption data in real time, and adjust energy supply dynamically. Unfortunately, real-time data may divulge users’ private information. To protect the privacy of real-time data, data aggregation schemes have been proposed to assist the control center in adjusting the supply to meet users’ electricity demands without sacrificing data privacy. However, extreme weather events and potential attacks may damage the meters and change the structure of the smart grid dynamically, it is important to improve the reliability of the data aggregation scheme. Even if some schemes have improved reliability, but the scalability is poor, they are not suitable for the dynamically changing smart grid network structure. To meet this end, a resilient data aggregation scheme for the smart grid is proposed, which 1) offers better reliability by defending against malicious attacks, group management techniques are proposed, and meters can update their keys when the smart grid network structure changes; 2) affords higher scalability; and 3) enables the control center to make fine-grained adjustments. A prototype implementation shows that the proposed scheme is efficient enough for smart meters.