Biblio

In the smart grid, the sharing of power data among various energy entities can make the data play a higher value. However, there may be unauthorized access while sharing data, which makes many entities unwilling to share their data to prevent data leakage. Based on blockchain and ABAC (Attribute-based Access Control) technology, this paper proposes an access control scheme, so that users can achieve fine-grained access control of their data when sharing them. The solution uses smart contract to achieve automated and reliable policy evaluation. IPFS (Interplanetary File System) is used for off-chain distributed storage to share the storage pressure of blockchain and guarantee the reliable storage of data. At the same time, all processes in the system are stored in the blockchain, ensuring the accountability of the system. Finally, the experiment proves the feasibility of the proposed scheme.

Hierarchical Text classification has recently become increasingly challenging with the growing number of classification labels. In this paper, we propose a hierarchical fine-tuning based approach for hierarchical text classification. We use the ordered neurons LSTM (ONLSTM) model by combining the embedding of text and parent category for hierarchical text classification with a large number of categories, which makes full use of the connection between the upper-level and lower-level labels. Extensive experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost.

The dummy-based method has been commonly used to protect the users location privacy in location-based services, since it can provide precise results and generally do not rely on a third party or key sharing. However, the close spatiotemporal correlation between the consecutively reported locations enables the adversary to identify some dummies, which lead to the existing dummy-based schemes fail to protect the users location privacy completely. To address this limit, this paper proposes a new algorithm to produce dummy location by generating dummy trajectory, which naturally takes into account of the spatiotemporal correlation all round. Firstly, the historical trajectories similar to the user's travel route are chosen as the dummy trajectories which depend on the distance between two trajectories with the help of home gateway. Then, the dummy is generated from the dummy trajectory by taking into account of time reachability, historical query similarity and the computation of in-degree/out-degree. Security analysis shows that the proposed scheme successfully perturbs the spatiotemporal correlation between neighboring location sets, therefore, it is infeasible for the adversary to distinguish the users real location from the dummies. Furthermore, extensive experiments indicate that the proposal is able to protect the users location privacy effectively and efficiently.