Cloud computing enables enterprises and individuals to outsource and share their data. This way, cloud computing eliminates the heavy workload of local information infrastructure. Attribute-based encryption has become a promising solution for encrypted data access control in clouds due to the ability to achieve one-to-many encrypted data sharing. Revocation is a critical requirement for encrypted data access control systems. After outsourcing the encrypted attribute-based ciphertext to the cloud, the data owner may want to revoke some recipients that were authorized previously, which means that the outsourced attribute-based ciphertext needs to be updated to a new one that is under the revoked policy. The integrity issue arises when the revocation is executed. When a new ciphertext with the revoked access policy is generated by the cloud server, the data recipient cannot be sure that the newly generated ciphertext guarantees to be decrypted to the same plaintext as the originally encrypted data, since the cloud server is provided by a third party, which is not fully trusted. In this article, we consider a new security requirement for the revocable attribute-based encryption schemes: integrity. We introduce a formal definition and security model for the revocable attribute-based encryption with data integrity protection (RABE-DI). Then, we propose a concrete RABE-DI scheme and prove its confidentiality and integrity under the defined security model. Finally, we present an implementation result and provide performance evaluation which shows that our scheme is efficient and practical.

To manage outsourced encrypted data sharing in clouds, attribute-based proxy re-encryption (ABPRE) has become an elegant primitive. In ABPRE, a cloud server can transform an original recipient’s ciphertext to a new one of a shared user’s. As the transformation is computation consuming, a malicious cloud server may return an incorrect re-encrypted ciphertext to save its computation resources. Moreover, a shared user may accuse the cloud server of returning an incorrect re-encrypted ciphertext to refuse to pay the cost of using the cloud service. However, existing ABPRE schemes do not support a mechanism to achieve verifiability and fairness. In this article, a novel verifiable and fair attribute-based proxy re-encryption (VF-ABPRE) scheme is introduced to support verifiability and fairness. The verifiability enables a shared user to verify whether the re-encrypted ciphertext returned by the server is correct and the fairness ensures a cloud server escape from malicious accusation if it has indeed conducted the re-encryption operation honestly. Additionally, we conduct a performance experiment to show the efficiency and practicality of the new VF-ABPRE scheme.

Phishing scams are one of Ethereum's most representative security risks that can defraud many transactions in a short period and severely threaten network security. Existing deep learning-based phishing scam detection methods mainly rely on constructing static transaction graphs which are assumed to be accessible before model training. However, static methods that have a high false positive rate to detect newly generated phishing scams by adding this newly generated data to existing algorithms for execution, due to new accounts and transactions constantly appearing in the real-world Ethereum network. Therefore, this paper, for the first time, proposes a novel evolve-based phishing scams detection method (named GrabPhisher) that extracts temporal features of accounts and captures information about the dynamic topology of the graph as it evolves. Specifically, GrabPhisher can build the evolutionary pattern of accounts trading on Ethereum as a diffusion network graph in continuous time. It can continue to capture new transaction features based on existing transactions, which facilitates the identification of phishing accounts. Additionally, we implement GrabPhisher on the real-world Ethereum phishing scams datasets. Extensive experimental results demonstrate that GrabPhisher can effectively extract dynamic temporal features and outperform state-of-the-art methods (95% Recall, and 88% F1-score).

Outsourcing the heavy decryption computation to a cloud service provider has been a promising solution for a resource-constrained mobile device to deploy an attribute-based encryption scheme. However, the current attribute based encryption with outsourced decryption schemes only enable the mobile device to verify whether the cloud service provider has returned a correct decryption result, they lack a mechanism to enable the cloud service provider to escape from a mobile device's wrong claim if it has returned a correct decryption result. This paper, for the first time, proposes an attribute based encryption with reliable outsourced decryption scheme using the blockchain smart contract. In the proposed scheme, not only can the mobile device verify whether the cloud service provider has returned a correct decryption result, but also the cloud service provider can escape from a wrong claim if the returned decryption result is correct. Moreover, our system achieves the fairness property, which means the cloud service provider can get the reward from the mobile device if and only if it has returned a correct decryption result. Finally, we conduct an implementation to demonstrate that the proposed scheme is practical and efficient.

Industrial Internet of Things (IIoT) systems are key enabling infrastructures that sustain the functioning of production and manufacturing. To satisfy the intelligence demands, federated learning has been envisioned as a promising technique for IIoT applications with privacy training requirements. However, research works have shown that, by training the local model on crafted poisoning samples malicious participants can jeopardize the functionalities of the global model. In this article, we propose a robust federated learning method, named RobustFL, in IIoT systems to defend against poisoning attacks. The main idea is that we conduct an adversarial training framework, in which an extra logits-based predictive model is built at the server-side to predict which participant a given logit belongs to. Meanwhile, the federated model is adversarially trained to prevent this predictive behavior, thus mitigating the poisoning attack influences. We evaluate the poisoning attack and our defense method on three benchmark datasets. Experimental results demonstrate the superiority of our proposed method in terms of high accuracy and efficiency in defending against poisoning attacks.

Cloud computing, which provides adequate storage and computation capability, has been a prevalent information infrastructure. Secure data sharing is a basic demand when data was outsourced to a cloud server. Attribute-based proxy re-encryption has been a promising approach that allows secure encrypted data sharing on clouds. With attribute-based proxy re-encryption, a delegator can designate a set of shared users through issuing a re-encryption key which will be used by the cloud server to transform the delegator's encrypted data to the shared users'. However, the existing attribute-based proxy re-encryption schemes lack a mechanism of revoking users from the sharing set which is critical for data sharing systems. Therefore, in this paper, we propose a concrete attribute-based proxy re-encryption with direct revocation mechanism (ABPRE-DR) for encrypted data sharing that enables the cloud server to directly revoke users from the original sharing set involved in the re-encryption key. We implemented the new schemes and evaluated its performance. The experimental results show that the proposed ABPRE-DR scheme is efficient and practical.