Cloud computing provides users with convenient data storage services, which simultaneously poses various security concerns, the integrity of outsourced data has been termed as one of the most concerning security issues. Certificateless public auditing not only enables a third-party auditor (TPA) to check data integrity, but also avoids complex certificates management and inherent drawbacks of key escrow. Up to date, a few certificateless public auditing schemes have been proposed, without providing fast data dynamics or protecting the identity privacy of users. In this paper, we propose a lightweight conditional anonymous certificateless public auditing (CACPA) scheme, supporting much faster data dynamics in cloud storage systems. Based on a homomorphic hash function, we design a certificateless signature and integrate it into the construction of CACPA, reducing the computational costs of TPA substantially. CACPA achieves conditional identity privacy preservation, anyone cannot infer the real identity of a user based on outsourced data, only the private key generator (PKG) can revoke the users when some misbehaviors occur. We provide security analysis of CACPA, and conduct performance evaluation demonstrating the lightweight advantages of CACPA, and therefore it is suitable for auditors with resource-constrained mobile devices.

Encrypted data aggregation is an effective approach to preserve power consumption data confidentiality as well as reduce communication overhead in smart grids. As the core component of smart grids, smart meters periodically report encrypted multidimensional data to an aggregator gateway. However, heavy computation and communication overhead may hinder the wide applications of smart meters. Besides, once the secret key is leaked, encrypted data will be broken, thereby violating users’ privacy. In this article, we present a lightweight multidimensional encryption data aggregation (MD-EDA) scheme, enabling a grid control center to retrieve fine-grain aggregated data in a confidential way. In particular, each fog node is installed at the edge of networks, playing the role of aggregator gateway for data verification and aggregation in the same grid area. MD-EDA prevents adversaries from extracting valuable data even if the secret key is compromised. MD-EDA exploits Shamir secret-sharing technique to achieve transmission fault tolerance mechanism from smart meters to a corresponding fog node. The performance evaluation demonstrates the lightweight computation advantages of MD-EDA in the deployment of fog-assisted smart grids.

Edge computing builds relevant services and applications on the edge server near the user side, which enables a faster service response. However, the lack of large-scale hardware resources leads to weak defense for edge devices. Therefore, proactive defense security mechanisms, such as Intrusion Detection Systems (IDSs), are widely deployed in edge computing. Unfortunately, most of those IDSs lack causal analysis capabilities and still suffer the threats from Advanced Persistent Threat (APT) attacks. To effectively detect APT attacks, we propose a heterogeneous graph neural networks threat detection model based on the provenance graph. Specifically, we leverage the powerful analysis and tracking capabilities of the provenance graph to model the long-term behavior of the adversary. Moreover, we leverage the predictive power of heterogeneous graph neural networks to embed the provenance graph by a node-level and semantic-level heterogeneous mutual attention mechanism. In addition, we also propose a provenance graph reduction algorithm based on the semantic similarity of graph substructures to improve the detection efficiency and accuracy of the model, which reduces and integrates redundant information by calculating the semantic similarity between substructures. The experimental results demonstrate that the prediction accuracy of our method reaches 99.8% on the StreamSpot dataset and achieves 98.13% accuracy on the NSL-KDD dataset.

Federated learning with a distributed trust framework effectively mitigates centralized security risks. However, it remains vulnerable to in-protocol DoS attacks, resulting in the malicious server refusing to aggregate the valid gradients or terminating the protocol. Additionally, it is susceptible to collaborative attacks, where compromised servers and clients can bypass gradient verification to inject backdoors. To address those issues, we propose a robust and secure aggregation scheme for federated learning, which extends the efficient 2-party computation (2PC) to a 3-party computation (3PC) with at most one malicious party, resisting abnormal termination and colluding poisoning attacks. In particular, we skillfully combine the replicated secret sharing with L2 and L∞ defense, ensuring the malformed gradients filtering with a non-interactive setup. Moreover, we integrate the player elimination framework to detect misbehavior and guarantee output delivery. The formal security analysis proves that our scheme maintains malicious security even under the colluding model. Extensive experiments demonstrate that RoseFL is more client-friendly and significantly enhances client efficiency by approximately 4 orders of magnitude compared to state-of-the-art methods.

Encrypted medical data outsourced to cloud servers can be used for personal health certification, health monitoring, and medical research. These data are essential to support the development of the medical industry. However, the traditional peer-to-peer data-sharing paradigm can lead to data abuse by malicious data analysis centers. Moreover, the encryption used to protect users' outsourced privacy restricts the flexibility of data retrieval. Based on the modified double trapdoor cryptosystem, we propose an authorized data retrieval scheme over aggregated encrypted medical data (ADR-AED) in cloud-assisted e-healthcare systems. In ADR-AED, patients can access and decrypt personal data and authorize the data analysis center (DAC) to retrieve corresponding data. Specifically, we design an authorized retrieval-test mechanism for a group of patients to DAC. This allows DAC to extract valuable information from a threshold number of authorized users. Additionally, each patient can flexibly retrieve finegrained medical data in different periods and submit them to a doctor for diagnostic analysis. The security analysis and performance evaluation demonstrate the feasibility of ADRAED in the deployment of cloud-assisted e-healthcare systems