To obtain carbon peaking and carbon neutrality goals, it is necessary to establish a national carbon emission platform for monitoring and reporting carbon dioxide equivalent. Energy data contains a good deal of important data and hence is required to be securely shared. Secure data aggregation solutions are an effective method that enables the process of adding up the contributions from several parties without revealing their inputs. Most of these solutions can be achieved using Paillier encryption, which inherently has multiple rounds of communication for aggregating ciphertexts. In this work, we present a privacy-preserving and verifiable secure data aggregation for a national carbon emissions monitoring service system, which can be verifiable encryption. To achieve verification, we apply zero-knowledge proofs to enable underlying threshold encryption and bilinear aggregate signature to detect the validity of the summation results. Finally, we show the correctness, verification, and informal security analysis of our solution. Compared with the existing scheme, our work can reduce the number of rounds for aggregating ciphertexts and is suitable for the national carbon emission platform.
Attribute-based access control models are widely used in permission management for resource access. By mining access control lists of policies, it can significantly reduce the cost of policy management and streamline the composition of policy rules. However, as resources increase, access policies will become complex. Uncontrolled attribute will lead to policy conflicts and thus policy mining will no longer be reliable. To address this issue, we propose a dynamic access control model based on attribute reachability. Firstly, we analysed the accessibility of attributes to ensure the reliability of authorised attributes. Secondly, we propose a multi-dimensional attribute management mechanism based on precondition-limited attributes, which enables permission passing and inheritance. On the basis of reachability access policy, we finally achieved secure policy mining changes by traversing user permission relationship tuples and constructing candidate rule seeds.
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