Neural network inference in cloud service offers tangible benefits to users, from individuals and small institutions to large companies. However, two crucial concerns must be addressed. The first arises in satisfying the privacy of the model, the input data, and the inference results throughout the inference process. The second pertains to verifying that the inferences are derived from the designated neural network model. Although Secure Multi-Party Computation (MPC) and Zero-Knowledge Proof (ZKP) are typically adopted to mitigate such issues, the major challenge lies in achieving privacy preservation and verifiability simultaneously. In this study, we address both issues by proposing VSecNN, a verifiable and privacy-preserving neural network inference scheme. Specifically, we integrate MPC with the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) protocol to achieve zero-knowledge proof generation for multiple parties. Subsequently, we perform adaptive optimizations on the multi-party proof generation approach to align with the neural network, thereby achieving both privacy-preserving capabilities and verifiability. Experimental results demonstrate an improvement in the efficiency. For example, the computation time for completing our multi-party proof generation could be as low as 1.7 times that of the single-party proof generation, while the verification requires only 169ms on the MNIST dataset.

Unlike a traditional centralized and producer-controlled power grid, the smart grid is a more complicated distributed power system consisted of many resources and applications. In smart grid, huge amounts of data generated by edge devices are collected by different parties. To achieve high operation efficiency, it is important to enable the data sharing and cooperative computation among different parties. How to protect the security and privacy of the utility data and the identities of their owners has become a major concern. There have been some studies on this issue. However, most of these works failed to consider the privacy protection in the dual sides of the data owner and receiver. In this article, we propose BPM4SG, a blockchain-based dual-side privacy-preserving multiparty computation (MPC) scheme for edge-enabled smart grid. In BPM4SG, the data segmentation method is adopted to ensure the security of MPC (e.g., summation) in edge nodes. The consortium blockchain and smart contract are used to further increase the system security and avoid the dependency on trusted third parties. Additionally, a data obfuscation method based on the ring signatures and a new one-time address scheme are proposed to protect the privacy of both the data owner and data receiver. The analysis shows that BPM4SG can meet the security and privacy requirements of smart grid. The experimental evaluation results demonstrate that our scheme has a better performance compared with other popular schemes.

Federated Learning (FL), which allows multiple participants to co-train machine Learning models without exposing local data, has been recognized as a promising method in the past few years. However, in the FL process, the server side may steal sensitive information of users, while the client side may also upload malicious data to compromise the training of the global model. Most existing privacy-preservation FL schemes seldom deal with threats from both of these two sides at the same time. In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed framework of FL. Homomorphic encryption and Multi-Krum technology are combined to achieve ciphertext-level model aggregation and model filtering, which can guarantee the verifiability of local models while realizing privacy-preservation. Security analysis and performance evaluation prove that the proposed scheme can achieve enhanced security and improve the performance of the FL model.

Nowadays, the advance of smart grid technology has fostered the development of microgrids, which can efficiently control and manage the distributed energy resources (DERs). In smart grid, IoT devices generate huge amounts of data, which are collected and shared among DERs, microgrids, and the main grid. To protect the shared data, it is necessary to implement the secure and efficient data access control. Ciphertext policy attribute-based encryption (CP-ABE) is a promising solution for the distributed system. However, lightweight IoT devices with limited computing capability cannot handle the computationally intensive ABE algorithms. To overcome this constraint, the decryption phase of CP-ABE is usually outsourced to the cloud, but this is inefficient and not safe enough in the distributed environment. In this article, we propose an edge blockchain empowered secure data access control scheme with fair accountability for the smart grid. The computation workloads of end user devices are outsourced to the edge nodes in a consortium blockchain system We adopt an on-chain/off-chain approach to ensure the flexible data sharing. Additionally, we adopt the threshold secret sharing scheme to establish a distributed authority. The security analysis and performance evaluation are conducted to prove the security and efficiency of our scheme. We use the Raspberry Pi to simulate lightweight IoT devices in the Hyperledger fabric platform to prove the usability of our scheme.

With the development of information and communication technologies in smart grid, peer-to-peer (P2P) energy trading for distributed energy resources (DER) has achieved an efficient two-way flow of information and power. The adoption of blockchain technology makes the P2P energy trading more secure and transparent. Considering that users with extra energy may be reluctant to participate in the energy trading due to privacy concerns, many researchers have focused on potential privacy issues. However, most of the existing works are built on top of a semi-decentralized energy blockchain in which only a few certified third-party nodes are authorized to manage and verify transactions - once these authorized nodes are attacked, the system will be threatened. In this paper, we use the Ciphertext Policy Attribute-Based Encryption (CP-ABE) scheme to establish a blockchain based P2P energy trading approach with privacy preservation, which allows peer nodes, including sellers and purchasers, to manage and verify transactions autonomously without needing any additional third-party nodes. In addition, we introduce the redactable blockchain technology into our scheme to ensure users can modify their sensitive information uploaded to the blockchain. Furthermore, we improve the CP-ABE scheme to provide low latency in the system. The experimental evaluations show that our scheme is efficient and practical.

The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large number of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.