Detecting the community structure and predicting the change of community structure is an important research topic in social network research. Focusing on the importance of nodes and the importance of their neighbors and the adjacency information, this article proposes a new evaluation method of node importance. The proposed overlapping community detection algorithm (ILE) uses the random walk to select the initial community and adopts the adaptive function to expand the community. It finally optimizes the community to obtain the overlapping community. For the overlapping communities, this article analyzes the evolution of networks at different times according to the stability and differences of social networks. Seven common community evolution events are obtained. The experimental results show that our algorithm is feasible and capable of discovering overlapping communities in complex social network efficiently.

Consensus protocol is one of the core technologies of Internet of Things (IoT)-blockchain applications, which is used to ensure the consistency of data between terminal devices that do not trust each other. Practical Byzantine fault tolerance (PBFT) is a typical consensus algorithm. Due to its advantages of low computational power and complexity, PBFT is deemed more suitable for IoT-blockchain applications. PBFT can tolerate 1/3 faulty nodes in a blockchain network, which can be malicious or unresponsive. In this work, if a node does not respond to messages from other nodes, it can be regarded as an offline node. Therefore, when more than a third of the nodes go offline, the blockchain network breaks down. However, in IoT applications, this situation is likely to occur and greatly limits the security and stability of IoT-blockchain networks. In order to solve the above problem, we propose a novel threshold proxy signature-based PBFT (TP-PBFT) consensus for IoT-blockchain applications. We construct a new threshold proxy signature scheme that enables the proxy signers to sign messages on behalf of the offline nodes. In addition, we design a “two-step clustering” method to construct a double-layer architecture that improves the scalability of PBFT. Meanwhile, a reputation mechanism is introduced to evaluate the quality of the nodes. The experimental results show that our TP-PBFT consensus protocol can reach consensus when the number of offline nodes more than 1/3.

Additively Homomorphic Encryption (AHE) has been widely used in various applications, such as federated learning, blockchain, and online auctions. Elliptic Curve (EC) based AHE has the advantages of efficient encryption, homomorphic addition, scalar multiplication algorithms, and short ciphertext length. However, EC-based AHE schemes require solving a small exponential Elliptic Curve Discrete Logarithm Problem (ECDLP) when running the decryption algorithm, i.e., recovering the plaintext m ∈ {0, 1} ^ℓ from m * G . Therefore, the decryption of EC-based AHE schemes is inefficient when the plaintext length ℓ > 32. This leads to people being more inclined to use RSA-based AHE schemes rather than EC-based ones. This paper proposes an efficient algorithm called FastECDLP for solving the small exponential ECDLP at 128-bit security level. We perform a series of deep optimizations from two points: computation and memory overhead. These optimizations ensure efficient decryption when the plaintext length ℓ is as long as possible in practice. Moreover, we also provide a concrete implementation and apply FastECDLP to some specific applications. Experimental results show that FastECDLP is far faster than the previous works. For example, the decryption can be done in 0.35 ms with a single thread when ℓ = 40, which is about 30 times faster than that of Paillier. Furthermore, we experiment with ℓ from 27 to 54, and the existing works generally only consider ℓ ≤ 32. The decryption only requires 1 second with 16 threads when ℓ = 54. In the practical applications, we can speed up model training of existing vertical federated learning frameworks by 4 to 14 times. At the same time, the decryption efficiency is accelerated by about 140 times in a blockchain financial system (ESORICS 2021) with the same memory overhead.

Vertical Federated Learning (VFL) has many applications in the field of smart healthcare with excellent performance. However, current VFL systems usually primarily focus on the privacy protection during model training, while the preparation of training data receives little attention. In real-world applications, like smart healthcare, the process of the training data preparation may involve some participant’s intention which could be privacy information for this participant. To protect the privacy of the model training intention, we describe the idea of Intention-Hiding Vertical Federated Learning (IHVFL) and illustrate a framework to achieve this privacy-preserving goal. First, we construct two secure screening protocols to enhance the privacy protection in feature engineering. Second, we implement the work of sample alignment bases on a novel private set intersection protocol. Finally, we use the logistic regression algorithm to demonstrate the process of IHVFL. Experiments show that our model can perform better efficiency (less than 5min) and accuracy (97%) on Breast Cancer medical dataset while maintaining the intention-hiding goal.

As the next‐generation power grid system, the smart grid can realize the balance of supply and demand and help in communication security and privacy protection. However, real‐time power consumption data collection might expose the users’ privacy information, such as their living habits and economic conditions. In addition, during the process of data transmission, it may lead to data inconsistency between the user side and the storage side. Blockchain provides tamper‐resistant and traceable characteristics for solving these problems, and ring signature schemes provide an anonymous authentication mechanism. Therefore, in this work, we consider the applications of ring signature scheme in smart grid based on blockchain. We introduce the notion of multi‐authority traceable ring signature (MA‐TRS) scheme for distributed setting. In our scheme, there is an auditing node that can distinguish the identity of the real signer from the ring without any secret information. Last but not least, we prove that the proposed scheme is unforgeable, anonymous, and traceable.

The revocable ciphertext-policy attribute-based encryption (R-CP-ABE) is an extension of ciphertext-policy attribute-based encryption (CP-ABE), which can realize user direct revocation and maintain a short revocation list. However, the revoked users can still decrypt the previously authorized encrypted data with their old key. The R-CP-ABE scheme should provide a mechanism to protect the encrypted data confidentiality by disqualifying the revoked users from accessing the previously encrypted data. Motivated by practical needs, we propose a new user R-CP-ABE scheme that simultaneously supports user direct revocation, short revocation list, and ciphertext update by incorporating the identity-based and time-based revocable technique. The scheme provides a strongly selective security proof under the modified decisional -parallel bilinear Diffie–Hellman Exponent problem, where “strongly” means that the adversary can query the secret key of a user whose attribute set satisfies the challenge ciphertext access structure and whose identity is in the revocation list.